Senior Software Engineer, Platform Infrastructure

🏢 Summary: Design and build a GPU-accelerated compute orchestration platform for distributed AI training, inference, and large-scale research workloads. The role focuses on managing GPU clusters, bare-metal provisioning, and intelligent resource scheduling with cloud-like simplicity. You will develop scalable, high-performance systems enabling programmatic access to multi-tenant AI infrastructure. 🗂️ Requirements: 5+ years software engineering experience, Experience building compute orchestration or distributed compute systems, Strong Kubernetes knowledge and workload deployment experience, Proficiency in Go, C/C++, Python, or Rust, Strong Linux systems programming experience, Experience with virtualization and container technologies, Understanding of GPU architectures and resource management, Experience with bare-metal provisioning and lifecycle management, Ability to design scalable, high-performance systems 📃 Skills: Go, C++, Python, Rust, Kubernetes, Docker, KVM, Xen, CUDA, NVIDIA, GPUDirect, SR-IOV, vGPU, InfiniBand, RDMA, NVLink, NVSwitch, Terraform, FastAPI, gRPC, Linux, SLURM 🏢 Description: Moonlite delivers high-performance AI infrastructure for organizations running intensive computational research, large-scale model training, and demanding data processing workloads.We provide infrastructure deployed in our facilities or co-located in yours, delivering flexible on-demand or reserved compute that feels like an extension of your existing data center. Our team of AI infrastructure specialists combines bare-metal performance with cloud-native operational simplicity, enabling research teams and enterprises to deploy demanding AI workloads with enterprise-grade reliability and compliance. Your Role: You will be instrumental in building out our GPU-accelerated compute platform that powers distributed AI training and inference, large-scale simulations, and computational research workloads. Working closely with product, your platform team members, and infrastructure specialists, you'll design and implement the compute orchestration layer that manages GPU clusters, bare-metal provisioning, and resource scheduling-enabling researchers and engineers to programmatically access high-performance compute resources with cloud-like simplicity. Job Responsibilities Compute Orchestration Systems: Design and build scalable compute orchestration platforms that manage GPU clusters, bare-metal server provisioning, and resource allocation across co-located infrastructure environments. Resource Management & Scheduling: Implement intelligent workload scheduling, resource allocation, and optimization algorithms that maximize GPU utilization while maintaining performance guarantees for research and training workloads. Research Cluster Provisioning: Design and implement systems for provisioning and managing research computing environments including Kubernetes and SLURM clusters, enabling automated deployment, resource scheduling, and workload orchestration for distributed AI training and HPC workloads. GPU Platform Engineering: Develop platform capabilities for managing latest-generation NVIDIA GPU configurations (H100, H200, B200, B300), including GPU resource management, multi-tenant isolation, and integration with compute orchestration systems. Bare-Metal Lifecycle Management: Build automation and tooling for complete bare-metal server lifecycle management – from initial provisioning and configuration through ongoing operations, updates, and resource reallocation. Performance-Critical Systems: Optimize compute platform components for high-throughput and low-latency performance, ensuring research workloads achieve near-bare-metal efficiency in virtualized or containersized environments. Platform APIs & Integration: Develop robust APIs and SDKs that enable researchers to programmatically provision and manage compute resources, integrating seamlessly with existing workflows and research infrastructure. Observability & Monitoring: Implement comprehensive monitoring and telemetry systems for compute resources, providing visibility into GPU virtualization, workload performance and infrastructure health. Multi-Tenancy and Isolation: Build enterprise-grade multi-tenant compute isolation, security boundaries, and resource quotas that enable safe sharing of GPU infrastructure across teams and organizations. Requirements Experience: 5+ years in software engineering with proven experience building compute platforms, container orchestration systems, or distributed compute infrastructure for production environments. Compute Platform Engineering: Strong background in building compute orchestration, resource scheduling, or workload management systems at scale. Kubernetes & Container Orchestration: Strong familiarity with Kubernetes architecture, container orchestration concepts, and experience deploying workloads in Kubernetes environments. Understanding of pods, deployments, services, and basic Kubernetes operations. Programming Skills: Experience with Go, C/C++, Python, or Rust for performance-critical components is highly valued. Linux & Systems Programming: Strong experience with Linux in production environments, including systems for programming, performance optimization, and low-level resource management. Virtualization & Containers: Deep knowledge of virtualization technologies (KVM, Xen), container runtimes, and orchestration platforms. GPU Computing Fundamentals: Understanding of GPU architectures, CUDA programming (where/when needed), and GPU resource management – or a strong ability to learn quickly. Bare-Metal Infrastructure: Experience with bare-metal provisioning, out-of-band management systems, and hardware abstraction layers. Problem-Solving & Architecture: Demonstrated ability to solve complex performance and scalability challenges while balancing pragmatic shipping with good long-term architecture. Autonomy & Communication: Comfortable navigating ambiguity, defining requirements collaboratively, and communicating technical discussions through clear documentation. Commitment to Growth: Growth mindset with continuous focus on learning and professional development. Preferred Qualifications Background provisioning or managing research computing environments (Kubernetes, SLURM, or HPC clusters) Experience with GPU virtualization technologies (SR-IOV, NVIDIA vGPU) and multi-tenant GPU sharing Background in container orchestration platforms with custom scheduling or resource management Knowledge of high-performance networking for GPU communication (InfiniBand, RDMA, NVLink, NVSwitch) Familiarity with AI/ML training frameworks (PyTorch, TensorFlow) and their infrastructure requirements Understanding of distributed training patterns and multi-node GPU coordination Experience building infrastructure for research institutions,labs, or technical computing environments Background in financial services or other regulated industry infrastructure is a plus Key Technologies Go, C/C++, Python, KVM, Docker, Kubernetes,, NVIDIA GPUDirect, SR-IOV, NVIDIA vGPU, CUDA, InfiniBand, RDMA, Terraform, FastAPI, gRPC, Linux systems programming Why Moonlite Build Next-Generation Infrastructure: Your work will create the platform foundation that enables financial institutions to harness AI capabilities previously impossible with traditional infrastructure. Hands-On Ownership: As an early engineer, you'll have end-to-end ownership of projects and the autonomy to influence our product and technology direction. Shape Industry Standards: Contribute to defining how enterprise AI infrastructure should work for the most demanding regulated environments. Collaborate with Experts: Work alongside seasoned engineers and industry professionals passionate about high-performance computing, innovation, and problem-solving. Start-Up Agility with Industry Impact: Enjoy the dynamic, fast-paced environment of a startup while making an immediate impact in an evolving and critical technology space. We offer a competitive total compensation package combining a competitive base salary, startup equity, and industry-leading benefits. The total compensation range for this role is $165,000 – $225,000, which includes both base salary and equity. Actual compensation will be determined based on experience, skills, and market alignment. We provide generous benefits, including a 6% 401(k) match, fully covered health insurance premiums, and other comprehensive offerings to support your well-being and success as we grow together. #li-remote

🏢 Summary: Engineering role focused on designing and building a GPU-accelerated compute orchestration platform for distributed AI training, inference, and large-scale research workloads. The position involves developing systems for GPU cluster management, bare-metal provisioning, intelligent scheduling, and multi-tenant resource isolation. You will create high-performance, cloud-like infrastructure enabling programmatic access to scalable compute resources. 🗂️ Requirements: 5+ years software engineering experience in compute platforms or distributed infrastructure, Experience building compute orchestration or resource scheduling systems at scale, Strong knowledge of Kubernetes architecture and container orchestration, Proficiency in Go, C/C++, Python, or Rust, Strong Linux systems programming experience, Experience with virtualization technologies and container runtimes, Understanding of GPU architectures and CUDA, Experience with bare-metal provisioning and lifecycle management 📃 Skills: Go, C++, Python, Rust, Kubernetes, Docker, KVM, Xen, CUDA, Linux, SLURM, Terraform, gRPC, FastAPI, InfiniBand, RDMA, NVLink, NVSwitch, GPUDirect, SR-IOV, vGPU 🏢 Description: Moonlite delivers high-performance AI infrastructure for organizations running intensive computational research, large-scale model training, and demanding data processing workloads.We provide infrastructure deployed in our facilities or co-located in yours, delivering flexible on-demand or reserved compute that feels like an extension of your existing data center. Our team of AI infrastructure specialists combines bare-metal performance with cloud-native operational simplicity, enabling research teams and enterprises to deploy demanding AI workloads with enterprise-grade reliability and compliance. Your Role: You will be instrumental in building out our GPU-accelerated compute platform that powers distributed AI training and inference, large-scale simulations, and computational research workloads. Working closely with product, your platform team members, and infrastructure specialists, you'll design and implement the compute orchestration layer that manages GPU clusters, bare-metal provisioning, and resource scheduling-enabling researchers and engineers to programmatically access high-performance compute resources with cloud-like simplicity. Job Responsibilities Compute Orchestration Systems: Design and build scalable compute orchestration platforms that manage GPU clusters, bare-metal server provisioning, and resource allocation across co-located infrastructure environments. Resource Management & Scheduling: Implement intelligent workload scheduling, resource allocation, and optimization algorithms that maximize GPU utilization while maintaining performance guarantees for research and training workloads. Research Cluster Provisioning: Design and implement systems for provisioning and managing research computing environments including Kubernetes and SLURM clusters, enabling automated deployment, resource scheduling, and workload orchestration for distributed AI training and HPC workloads. GPU Platform Engineering: Develop platform capabilities for managing latest-generation NVIDIA GPU configurations (H100, H200, B200, B300), including GPU resource management, multi-tenant isolation, and integration with compute orchestration systems. Bare-Metal Lifecycle Management: Build automation and tooling for complete bare-metal server lifecycle management – from initial provisioning and configuration through ongoing operations, updates, and resource reallocation. Performance-Critical Systems: Optimize compute platform components for high-throughput and low-latency performance, ensuring research workloads achieve near-bare-metal efficiency in virtualized or containersized environments. Platform APIs & Integration: Develop robust APIs and SDKs that enable researchers to programmatically provision and manage compute resources, integrating seamlessly with existing workflows and research infrastructure. Observability & Monitoring: Implement comprehensive monitoring and telemetry systems for compute resources, providing visibility into GPU virtualization, workload performance and infrastructure health. Multi-Tenancy and Isolation: Build enterprise-grade multi-tenant compute isolation, security boundaries, and resource quotas that enable safe sharing of GPU infrastructure across teams and organizations. Requirements Experience: 5+ years in software engineering with proven experience building compute platforms, container orchestration systems, or distributed compute infrastructure for production environments. Compute Platform Engineering: Strong background in building compute orchestration, resource scheduling, or workload management systems at scale. Kubernetes & Container Orchestration: Strong familiarity with Kubernetes architecture, container orchestration concepts, and experience deploying workloads in Kubernetes environments. Understanding of pods, deployments, services, and basic Kubernetes operations. Programming Skills: Experience with Go, C/C++, Python, or Rust for performance-critical components is highly valued. Linux & Systems Programming: Strong experience with Linux in production environments, including systems for programming, performance optimization, and low-level resource management. Virtualization & Containers: Deep knowledge of virtualization technologies (KVM, Xen), container runtimes, and orchestration platforms. GPU Computing Fundamentals: Understanding of GPU architectures, CUDA programming (where/when needed), and GPU resource management – or a strong ability to learn quickly. Bare-Metal Infrastructure: Experience with bare-metal provisioning, out-of-band management systems, and hardware abstraction layers. Problem-Solving & Architecture: Demonstrated ability to solve complex performance and scalability challenges while balancing pragmatic shipping with good long-term architecture. Autonomy & Communication: Comfortable navigating ambiguity, defining requirements collaboratively, and communicating technical discussions through clear documentation. Commitment to Growth: Growth mindset with continuous focus on learning and professional development. Preferred Qualifications Background provisioning or managing research computing environments (Kubernetes, SLURM, or HPC clusters) Experience with GPU virtualization technologies (SR-IOV, NVIDIA vGPU) and multi-tenant GPU sharing Background in container orchestration platforms with custom scheduling or resource management Knowledge of high-performance networking for GPU communication (InfiniBand, RDMA, NVLink, NVSwitch) Familiarity with AI/ML training frameworks (PyTorch, TensorFlow) and their infrastructure requirements Understanding of distributed training patterns and multi-node GPU coordination Experience building infrastructure for research institutions,labs, or technical computing environments Background in financial services or other regulated industry infrastructure is a plus Key Technologies Go, C/C++, Python, KVM, Docker, Kubernetes,, NVIDIA GPUDirect, SR-IOV, NVIDIA vGPU, CUDA, InfiniBand, RDMA, Terraform, FastAPI, gRPC, Linux systems programming Why Moonlite Build Next-Generation Infrastructure: Your work will create the platform foundation that enables financial institutions to harness AI capabilities previously impossible with traditional infrastructure. Hands-On Ownership: As an early engineer, you'll have end-to-end ownership of projects and the autonomy to influence our product and technology direction. Shape Industry Standards: Contribute to defining how enterprise AI infrastructure should work for the most demanding regulated environments. Collaborate with Experts: Work alongside seasoned engineers and industry professionals passionate about high-performance computing, innovation, and problem-solving. Start-Up Agility with Industry Impact: Enjoy the dynamic, fast-paced environment of a startup while making an immediate impact in an evolving and critical technology space. We offer a competitive total compensation package combining a competitive base salary, startup equity, and industry-leading benefits. The total compensation range for this role is $165,000 – $225,000, which includes both base salary and equity. Actual compensation will be determined based on experience, skills, and market alignment. We provide generous benefits, including a 6% 401(k) match, fully covered health insurance premiums, and other comprehensive offerings to support your well-being and success as we grow together. #li-remote

🏢 Summary: The role focuses on designing, building, and operating production-grade Kubernetes infrastructure on bare-metal to power large-scale AI and high-performance compute workloads. It involves deep integration of networking, storage, and GPU technologies while ensuring enterprise-grade reliability, automation, and observability across multi-region environments. The position emphasizes Kubernetes internals, infrastructure automation, and SRE practices for mission-critical AI systems. 🗂️ Requirements: 5+ years experience in SRE, DevOps, or infrastructure engineering operating production infrastructure at scale, Hands-on experience building and operating Kubernetes clusters on bare-metal infrastructure, Strong knowledge of Kubernetes internals including CRDs, operators, controllers, admission webhooks, and scheduling, Experience integrating CSI storage, CNI networking, SR-IOV, and GPU device plugins with Kubernetes, Strong Linux systems administration and troubleshooting skills, Proficiency with infrastructure-as-code tools for automation, Solid understanding of networking fundamentals (IPAM, DNS, DHCP, VLAN, VXLAN, routing, load balancing), Experience implementing monitoring and logging solutions in production environments, Understanding of SRE principles including SLIs, SLOs, SLAs, and incident management, Strong scripting skills in Go, Python, or Bash 📃 Skills: Kubernetes, Linux, Terraform, Ansible, Helm, Prometheus, Grafana, ELK, Go, Python, Bash, NVIDIA, SR-IOV, CSI, CNI, Calico, Cilium, Multus, Ceph, InfiniBand, RDMA, RoCE, etcd 🏢 Description: Moonlite delivers high-performance AI infrastructure for organizations running intensive computational research, large-scale model training, and demanding data processing workloads.We provide infrastructure deployed in our facilities or co-located in yours, delivering flexible on-demand or reserved compute that feels like an extension of your existing data center. Our team of AI infrastructure specialists combines bare-metal performance with cloud-native operational simplicity, enabling research teams and enterprises to deploy demanding AI workloads with enterprise-grade reliability and compliance. Your Role: You will be instrumental in building and operating production-grade AI infrastructure with deep Kubernetes expertise at its core. Working closely with our systems engineers, network engineers, and platform engineering team, you'll architect and operate the Kubernetes infrastructure that powers our control plane and orchestrates compute, storage, and networking at scale. This role requires deep understanding of Kubernetes internals, custom resource definitions (CRDs), storage and network integrations, and building production-grade clusters from the ground up (not just deploying in managed environments). You'll ensure enterprise-grade reliability while establishing the automation, observability, and operational practices. Job Responsibilities Kubernetes Infrastructure Engineering: Design, build, and operate production Kubernetes clusters on bare-metal infrastructure – including cluster bootstrapping, control plane architecture, etcd management, and scaling strategies for high-performance compute workloads. Kubernetes Networking & CNIs: Implement and operate custom Kubernetes networking solutions with SR-IOV for high-performance GPU interconnects, multi-tenancy isolation and advanced networking policies. Configure CNI plugins and network segmentation for research workloads. Custom Operators & Controllers: Develop and maintain custom Kubernetes operators and controllers for bare-metal provisioning, infrastructure lifecycle management, and resource orchestration across compute, storage, and networking domains. GPU Infrastructure Integration: Deploy and optimize NVIDIA GPU operators, device plugins, and other custom scheduling logic for GPU workload placement and utilization optimization. Platform Integration & Storage: Build deep integrations between Kubernetes and underlying infrastructure including CSI drivers for storage, custom admission controllers for policy enforcement, and scheduling extensions for specialized hardware placement. Infrastructure Automation: Design and implement automation using Terraform, Ansible, Helm, and custom operators to orchestrate infrastructure workflows and enable deployments across multiple regions. Production Operations & Reliability: Manage production bare-metal infrastructure across multiple regions. Build systems ensuring high availability, fault tolerance, and graceful degradation – establishing SLIs, SLOs, and monitoring to meet enterprise reliability commitments. Observability & Incident Response: Build comprehensive monitoring, logging, and alerting using Prometheus, Grafana, and ELK stack. Lead incident response, conduct postmortems, and implement preventative measures to improve reliability and reduce MTTR. Performance & Capacity Planning: Identify and resolve performance bottlenecks across infrastructure domains. Monitor utilization trends, forecast capacity needs, and optimize resource allocation for various workloads. Requirements Experience: 5+ years in SRE, DevOps, or infrastructure engineering roles with proven experience operating production infrastructure at scale. Kubernetes Infrastructure Expertise: Deep hands-on experience building and operating production Kubernetes clusters on bare-metal infrastructure – not just deploying workloads in managed clusters. Must understand cluster bootstrapping, control plane architecture, etcd operations, and scaling strategies. Kubernetes Internals & Integration: Strong understanding of Kubernetes internals including custom resource definitions (CRDs), operators, controllers, admission webhooks, and scheduling. Experience integrating storage (CSI drivers), networking (CNI, SR-IOV), and specialized hardware (GPU device plugins) with Kubernetes. Linux Systems Experience: Strong fundamentals in Linux systems administration, performance tuning, troubleshooting, and automation in production environments. Infrastructure Automation: Proficiency with infrastructure-as-code tools (Terraform, Ansible, Helm) and building automation to reduce operational overhead. Networking Fundamentals: Solid understanding of networking concepts including IPAM, DNS, DHCP, VLAN/VXLAN, routing, load balancing, and experience troubleshooting network issues in production. Observability & Monitoring: Experience building and maintaining comprehensive monitoring solutions using tools like Prometheus, Grafana, and centralized logging systems. Reliability Practices: Understanding of SRE principles including SLIs/SLOs/SLAs, error budgets, incident management, and blameless postmortems. Scripting & Automation: Strong scripting skills in Go, Python, or Bash for automation, tooling development, and operational efficiency. Problem-Solving Under Pressure: Demonstrated ability to troubleshoot complex issues under pressure, manage incidents effectively, and communicate clearly during outages. Collaboration & Communication: Excellent communication skills and ability to work across teams including systems engineers, network engineers, and software developers. Preferred Qualifications Experience building custom Kubernetes operators or controllers for infrastructure orchestration Deep familiarity with Kubernetes networking (Calico, Cilium, Multus), service mesh technologies, and network policy management Experience with GPU workload orchestration including NVIDIA GPU Operator, MIG, time-slicing, and device plugins Background with advanced Kubernetes features including custom schedulers, admission controllers, and API server extensions Experience with Kubernetes cluster federation or multi-cluster management Knowledge of high-performance networking technologies (InfiniBand, RDMA, RoCE) and their integration with Kubernetes Experience with enterprise storage systems (VAST, Lightbits, Ceph, or similar) Familiarity with configuration management at scale and GitOps practices Understanding of security best practices for Kubernetes and bare-metal infrastructure Experience operating infrastructure in regulated industries or co-located data center environments Background supporting research institutions, technical computing environments, or enterprise AI infrastructure Key Technologies Kubernetes, Linux, Terraform, Ansible, Prometheus, Grafana, ELK Stack, Go, Python, Bash, NVIDIA GPU Technologies, High-Performance Networking, Enterprise Storage Systems Why Moonlite Build Critical Research Infrastructure: Your work will directly enable quantitative research teams and AI practitioners to push the boundaries of what's possible in financial modeling and AI research. Enterprise Impact: Build and operate infrastructure that supports mission-critical research and AI workloads for leading financial institutions and research organizations. Technical Excellence: Join an infrastructure team focused on delivering enterprise-grade reliability while pushing the boundaries of high-performance computing capabilities. Hands-On Ownership: As part of our growing infrastructure team, you'll have significant ownership over critical systems and the autonomy to influence our operational practices and technology choices. Industry Leadership: Work alongside experienced infrastructure professionals who have built and operated systems for the most demanding computing environments. We offer a competitive total compensation package combining a competitive base salary, startup equity, and industry-leading benefits. The total compensation range for this role is $165,000 – $225,000, which includes both base salary and equity. Actual compensation will be determined based on experience, skills, and market alignment. We provide generous benefits, including a 6% 401(k) match, fully covered health insurance premiums, and other comprehensive offerings to support your well-being and success as we grow together. #li-remote

🏢 Summary: The role focuses on designing and building a high-performance Software-Defined Networking (SDN) platform for AI and research workloads, enabling scalable, isolated, and automated networking using DPU-accelerated infrastructure. You will develop orchestration, provisioning, and lifecycle management systems integrating Kubernetes, SLURM, and GPU clusters. The position emphasizes programmable networking, multi-tenancy, and low-latency connectivity for distributed computing environments. 🗂️ Requirements: 5+ years of software engineering experience in network platforms or SDN systems, Strong knowledge of Kubernetes networking architecture and CNI plugins, Deep understanding of TCP/IP, VLANs, VXLAN, BGP, OSPF, and data center networking, Proven experience with SDN concepts and network virtualization, Proficiency in Go and Python, Strong experience with Linux networking stack and Open vSwitch, Familiarity with DPU or SmartNIC architectures and SR-IOV, Understanding of RDMA, RoCE, InfiniBand, and low-latency networking, Experience designing scalable network orchestration or automation systems 📃 Skills: Go, Python, Kubernetes, CNI, TCP/IP, VLAN, VXLAN, BGP, OSPF, SDN, Linux, OpenvSwitch, SR-IOV, Bluefield, RDMA, RoCE, InfiniBand, Terraform, FastAPI, gRPC 🏢 Description: Moonlite delivers high-performance AI infrastructure for organizations running intensive computational research, large-scale model training, and demanding data processing workloads. We provide infrastructure deployed in our facilities or co-located in yours, delivering flexible on-demand or reserved compute that feels like an extension of your existing data center. Our team of AI infrastructure specialists combines bare-metal performance with cloud-native operational simplicity, enabling research teams and enterprises to deploy demanding AI workloads with enterprise-grade reliability and compliance. Your Role: You will be foundational to building our software-defined networking (SDN) platform that enables high-performance, isolated networking for distributed computing, model training, inference, and data-intensive workloads. Working closely with our network, infrastructure, and product teams, you'll design and implement the network orchestration and provisioning systems that manage DPU-accelerated networking, tenant isolation, and network lifecycle management – enabling researchers and engineers to access enterprise-grade networking with cloud-like simplicity. Job Responsibilities: Software-Defined Networking Architecture: Collaborate with infrastructure to design and build scalable SDN orchestration systems leveraging NVIDIA Bluefield-3 DPUs to deliver programmable, high-performance networking for AI workloads with hardware-accelerated forwarding isolation. Research Cluster Networking: Design and implement networking systems for research computing environments including Kubernetes and SLURM clusters, enabling high-performance connectivity, optimized network topology for distributed workloads, and seamless integration with cluster orchestration systems. Network Provisioning & Lifecycle Management: Implement automated SDN provisioning systems that handle VPC creation, subnet allocation, routing configuration, and network resource lifecycle from deployment through decommissioning. DPU Platform Engineering: Develop platform capabilities for managing Bluefield-3 DPUs including SR-IOV virtual function management, OVS offload configuration, network function deployment, and integration with compute orchestration systems. Multi-Tenancy & Network Isolation: Build enterprise-grade network isolation using VPCs, VXLAN, and hardware-accelerated forwarding to ensure complete tenant separation while maintaining high-performance connectivity for GPU clusters and distributed workloads. High-Performance Networking: Collaborate with infrastructure to optimize network paths for RDMA, RoCE, and GPU-to-GPU communication, ensuring minimal latency and maximum throughput for distributed training and large-scale computational workloads. Network APIs & Integration: Develop robust APIs and SDKs for network resource management that integrate seamlessly with compute and storage platforms, enabling programmatic network provisioning and configuration. Network Observability: Implement comprehensive network monitoring, telemetry, and troubleshooting systems that provide visibility into network performance, utilization, and tenant traffic patterns.Security & Policy Management: Build platform network security features including security groups, firewall rules, and policy enforcement that protect tenant workloads while enabling flexible network configuration. Requirements: Experience: 5+ years in software engineering with proven experience building network platforms, SDN systems, or network automation for production environments. Kubernetes Networking & Container Orchestration: Strong familiarity with Kubernetes networking architecture, CNI plugins, service networking, and network policies. Understanding of pod networking, services, ingress, and how Kubernetes manages network resources. Networking Expertise: Deep understanding of networking fundamentals including TCP/IP, VLANs, VXLAN, BGP, OSPF, routing protocols, and data center network architectures.Software-Defined Networking: Background in SDN concepts, network virtualization, overlay networks, and programmable networking technologies. Programming Skills: Experience with Go and Python for performance-critical networking components and services is highly valued. Linux Networking: Strong experience with Linux networking stack, including network namespaces, iptables/nftables, Open vSwitch, and kernel networking systems. DPU & SmartNIC Experience: Familiarity with DPU/SmartNIC architectures (Bluefield, or similar), SR-IOV, hardware offload capabilities, and programmable networking hardware – or strong ability to learn quickly. High-Performance Networking: Understanding of RDMA, RoCE, Infiniband, and low-latency networking requirements for distributed computing and GPU workloads. Problem-Solving & Architecture: Demonstrated ability to solve complex networking performance and scalability challenges while balancing pragmatic shipping with good long-term architecture. Autonomy & Communication: Comfortable navigating ambiguity, defining requirements collaboratively, and communicating technical decisions through clear documentation. Commitment to Growth: Growth mindset with continuous focus on learning and professional development. Preferred Qualifications Background provisioning or managing networking for research computing environments (Kubernetes, SLURM, or HPC clusters) Experience with NVIDIA Bluefield DPU programming and DOCA framework Background with network function virtualization (NFV) and service function chaining Knowledge of Kubernetes networking (CNI plugins, network policies, service mesh) Experience building network control planes or SDN controllers Familiarity with network automation frameworks and infrastructure-as-code for networking Understanding of data center fabric architectures (spine-leaf, CLOS topologies) Experience with network security and compliance requirements in regulated industries Background building networking for research institutions, HPC environments, or cloud providers Key Technologies Go, Python, NVIDIA Bluefield DPUs, Open vSwitch, VXLAN, SR-IOV, RDMA, RoCE, InfiniBand, BGP, Linux networking, Terraform, FastAPI, gRPC Why Moonlite Build Next-Generation Infrastructure: Your work will create the platform foundation that enables financial institutions to harness AI capabilities previously impossible with traditional infrastructure. Hands-On Ownership: As an early engineer, you'll have end-to-end ownership of projects and the autonomy to influence our product and technology direction. Shape Industry Standards: Contribute to defining how enterprise AI infrastructure should work for the most demanding regulated environments. Collaborate with Experts: Work alongside seasoned engineers and industry professionals passionate about high-performance computing, innovation, and problem-solving. Start-Up Agility with Industry Impact: Enjoy the dynamic, fast-paced environment of a startup while making an immediate impact in an evolving and critical technology space. We offer a competitive total compensation package combining a competitive base salary, startup equity, and industry-leading benefits. The total compensation range for this role is $165,000 – $225,000, which includes both base salary and equity. Actual compensation will be determined based on experience, skills, and market alignment. We provide generous benefits, including a 6% 401(k) match, fully covered health insurance premiums, and other comprehensive offerings to support your well-being and success as we grow together. #li-remote

🏢 Summary: The offer is for a Senior Platform Engineer responsible for designing, building, and operating scalable Kubernetes-based platforms on AWS. The role focuses on backend platform development, Infrastructure as Code with Terraform, CI/CD automation, and improving developer experience through self-service and multi-tenant solutions. It involves driving reliability, observability, and automation across a cloud-native ecosystem. 🗂️ Requirements: 5+ years of experience as a Platform Engineer, Strong hands-on experience with Kubernetes, including cluster operations, Strong programming skills in TypeScript or Go, Experience with AWS or other cloud platforms, Experience with Infrastructure as Code tools, preferably Terraform, Experience with CI/CD pipelines and deployment automation, Experience designing scalable multi-tenant systems, Strong understanding of API and platform architecture, Solid Linux and networking fundamentals, Experience with observability and monitoring solutions, Experience building self-service platform capabilities, Practical experience using AI-powered coding assistants, Work permit and residence in the European Union 📃 Skills: Kubernetes, AWS, Terraform, TypeScript, Go, Python, CI/CD, Linux, Networking, APIs, Docker, OpenTelemetry, Backstage, GitHub, Claude, Cursor 🏢 Description: 🟣 You will be: designing, building, and operating scalable Kubernetes-based platform infrastructure, developing backend platform services and reusable engineering components, building and maintaining cloud-native solutions (primarily on AWS), creating and evolving Infrastructure as Code solutions using Terraform, designing and implementing CI/CD pipelines and deployment automation, developing scalable multi-tenant platform capabilities for engineering teams, building self-service developer platform features and internal tooling, designing and maintaining APIs and platform architecture standards, improving platform reliability, observability, and monitoring capabilities, collaborating with engineering teams to improve developer experience and platform adoption, contributing to platform engineering best practices and technical standards, supporting container platform operations and orchestration processes, driving automation and operational excellence across the platform ecosystem. 🟣 Your profile: 5+ years of experience as a Platform Engineer, practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery, strong hands-on experience with Kubernetes, including cluster operations and orchestration concepts, strong backend engineering background, strong programming skills in TypeScript and/or Go; Python experience is a plus, solid experience with cloud platforms, preferably AWS, practical experience with Infrastructure as Code tools, preferably Terraform, experience with CI/CD pipelines and deployment orchestration, experience designing scalable multi-tenant systems, strong understanding of API and platform architecture, solid Linux and networking fundamentals, experience with observability and monitoring solutions, experience building reusable self-service platform capabilities for developers, good English communication skills (at least B2 level). 🟣 Nice to have: experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work, interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches, experience with Internal Developer Platforms (IDP), experience building Kubernetes operators and controllers, experience with Backstage or developer portal tooling, experience in container runtime or platform engineering, knowledge of event-driven and distributed systems architecture, experience with service mesh technologies and OpenTelemetry. Work from the European Union region and a work permit are required. 🟣 Recruitment Process: CV review – HR Call – Interview – Client Interview – Decision 🎁 Benefits 🎁 ✍ Development: development budget of up to 6,800 PLN, we fund certifications e.g.: AWS, Azure, ISTQB, PSM, access to Udemy, Safari Books Online and more, events and technology conferences, technology Guilds, internal training, Xebia Library, Xebia Upskill. 🩺 We take care of your health: private medical healthcare, multiSport card - we subsidise a MultiSport card, mental Health Support. 🤸‍♂️ We are flexible: flexible working hours, B2B or permanent contract, contract for an indefinite period.

🏢 Summary: On-site role in Wroclaw for a Kubernetes & Cloud Infrastructure Engineer focused on building and operating scalable, secure AI platform infrastructure. The position centers on managing Kubernetes clusters, AWS environments, and infrastructure as code to support GPU workloads and large language model deployments. You will enhance reliability, automation, and observability of cloud-native AI systems. 🗂️ Requirements: 4+ years in Cloud, DevOps, or Platform Engineering, Strong expertise in Kubernetes cluster operations and multi-tenancy, Experience with GPU scheduling and model serving infrastructure, Hands-on experience with AWS (networking, IAM, EKS, EC2), Proficiency in Python, Experience with Terraform and infrastructure as code, Ability to build and operate CI/CD pipelines, Experience implementing GitOps workflows, Experience defining and managing infrastructure SLOs, Fluent English, Legal right to work in the EU 📃 Skills: Kubernetes, AWS, EKS, EC2, IAM, Terraform, Helm, Kustomize, Python, CI/CD, GitOps, GPU, Docker, Linux, Networking, Observability 🏢 Description: Unleash the power of cloud-native infrastructure — revolutionize AI platforms with your expertise! Wroclaw-based opportunity with on-site work model As a Kubernetes & Cloud Infrastructure Engineer – AI Platform , you will be working for our client, a leader in AI innovation, building and managing critical infrastructure that supports advanced AI models and large language model workloads. Your work will directly impact the delivery, scalability, and security of cutting-edge AI solutions, empowering teams across the firm to harness AI's full potential. Join us and be part of shaping the future of intelligent technologies! Your main responsibilities: Build and operate scalable Kubernetes clusters supporting multi-tenancy, GPU workloads, and model serving. Manage AWS infrastructure, including networking, IAM, security, and cost optimization. Develop and maintain infrastructure as code utilizing tools like Terraform, Helm, and Kustomize. Implement and maintain CI/CD and GitOps workflows to streamline deployment pipelines. Build observability solutions for system health, utilization, latency, and platform performance monitoring. Automate scaling, capacity management, and enforce security and governance policies across cloud and on-premise environments. Define and monitor infrastructure Service Level Objectives (SLOs) to ensure reliability and performance. Collaborate closely with AI Platform Engineers, Data Scientists, and Research teams to support model inference and deployment infrastructure. Enable internal teams through platform tooling, onboarding, and self-service portals. You're ideal for this role if you have: 4+ years of experience in Cloud, DevOps, or Platform Engineering. Strong expertise in Kubernetes, including cluster operations, multi-tenancy, GPU scheduling, and Helm/Kustomize. Hands-on experience with AWS cloud services—networking, IAM, EKS, EC2, and cost management. Proficient in Python and infrastructure-as-code tools such as Terraform. Proven ability to build and operate CI/CD and GitOps workflows. Skilled in defining and managing infrastructure SLOs. Pragmatic, ownership-driven approach and comfortable working in ambiguous environments. It is a strong plus if you have: Experience supporting LLM inference workloads, model serving platforms, or GPU-backed infrastructure. Knowledge of RAG systems, vector databases, or AI-related data platforms. Background in high-performance or latency-sensitive environments. Relevant certifications such as CKA, CKAD, Solutions Architect, or similar. Language Required for the role: Fluent English Eligibility to work in this role: Only candidates with an existing legal right to work in the European Union will be considered for this role. #MAKEYourCareerBETTER Interested? Apply now and include your CV (preferably in English) along with a statement confirming your consent to the processing and storage of your personal data.

🏢 Summary: Responsible for operating, automating, and continuously improving a cloud-native AI platform infrastructure to ensure reliability, scalability, and security of AI-powered applications. The role focuses on Kubernetes operations, migration from Nomad, database and core service management, and building strong observability standards. You enable development teams to deliver production-grade AI services with high operational excellence. 🗂️ Requirements: Strong hands-on experience with Kubernetes in production, Experience operating databases and cloud-native services, Experience with infrastructure automation, Experience with monitoring and observability tools, Ability to manage and migrate workloads from Nomad to Kubernetes, Knowledge of reliability, scalability, and security best practices, Fluent English 📃 Skills: Kubernetes, Nomad, PostgreSQL, ClickHouse, Redis, Qdrant, Dify, Langfuse, Kubeblocks, Cnpg, Automation, Monitoring, Observability, Cloud, AI 🏢 Description: Your mission At VIER, we build and operate AI-powered applications that enable intelligent customer service experiences at scale. As part of our AI Platform Services team, you will take ownership of the reliable operation, scalability, and continuous evolution of our AI infrastructure landscape. Working with modern cloud-native technologies and AI-driven automation, you will help ensure that our applications, services, and Kubernetes environments meet the highest standards for reliability, security, and operational excellence — empowering development teams to deliver production-grade AI solutions with speed and confidence. In this position: You own the reliable day-to-day operation of platform infrastructure services within the AI Business Unit. You drive automation across operational workflows, using modern tooling and AI to improve efficiency, scalability, maintainability, and security. You support and contribute to the migration of internal applications from Nomad to Kubernetes. You collaborate with Infrastructure teams to continuously improve Kubernetes environments. You manage and support core technologies such as Qdrant, PostgreSQL, ClickHouse, Redis, Dify, Langfuse, Kubeblocks, and the Cnpg.io Postgres Operator. You build and improve monitoring, alerting, and operational observability standards. You enable development teams to design, ship, and operate production-grade Kubernetes-ready services. You improve system reliability, scalability, security, and operational excellence across the AI landscape. Your skills You are a pragmatic problem-solver with a strong focus on reliability, quality, security, ownership, and continuous improvement. You have strong hands-on experience with Kubernetes in production environments. You have experience operating databases and cloud-native services. You are familiar with monitoring and observability tools. You bring a strong automation mindset and follow a zero-downtime approach. You are curious and motivated to deeply understand the operational behavior of modern AI technologies. You speak fluent English. Why us? We are a company with flat hierarchies and plenty of freedom to make your own decisions and take ownership of your responsibilities. We offer an engaging and varied role where you can make a difference. We support professional development and training tailored to your needs. Flexible working hours and a trust-based work schedule make it easy to organize your daily work routine. A hybrid work model with three days of remote work. Appreciative interactions and respectful communication in line with our Guiding Principles. Regular and constructive feedback sessions that support your targeted development. We also offer support through our service provider INSITE during difficult life situations (professional or personal). Promotion of a diverse, inclusive corporate culture where all employees have equal opportunities. In addition, we offer a range of other benefits that enrich your daily work life (e.g., mobility allowance, sports programs, events, etc.). You can find more details on our careers page. *Gender-neutral: At our company, it doesn’t matter whether you’re female, male, or non-binary… where you’re from, what your skin color is, or what you believe in. We focus on mutual respect and appreciation, as well as fun and motivation at work. Let your personality shine—with us, you’re at the center of everything we do.

🏢 Summary: Mid Python Engineer role focused on building scalable backend services, REST APIs, and cloud-native solutions in Azure environments for large-scale data-driven products. The position involves working with Python, FastAPI, PostgreSQL, Docker, CI/CD pipelines, and Agentic AI integrations in global enterprise projects. Remote-first work model with opportunities for technical ownership, architecture contributions, and professional growth. 🗂️ Requirements: 2+ years commercial experience as a Python Engineer, Strong proficiency in Python, Experience with FastAPI or comparable async frameworks, Hands-on experience with PostgreSQL, Experience with Docker containerization, Experience deploying applications on Azure, Experience with GitHub Actions or similar CI/CD tooling, Understanding of spec-driven development, Exposure to Agentic AI or LLM-based systems, Clear communication skills in English, Ability to collaborate with technical and non-technical stakeholders 📃 Skills: Python, FastAPI, PostgreSQL, Docker, Azure, GitHub, CI/CD, REST, AI, LLM, MLOps, Terraform, Bicep, Microservices, DevOps 🏢 Description: Why Join Us At Cloudfide, we engineer solutions where data truly becomes an accelerator for innovation. We build and evolve large-scale Big Data and cloud-native platforms for global clients, including leaders in retail and the Fortune 500. We process massive, complex datasets, operate fully in the cloud ecosystem, and work with the latest technologies, including AI, ML, MLOps, and modern data architectures. Our projects have measurable, real‑world impact - from enhancing customer experiences at scale to empowering data-driven decision-making in high‑visibility global organizations. As we continue to grow, you'll have the opportunity to shape solutions, mentor others, drive architecture decisions, and take ownership of strategic initiatives. Opportunity Overview As a Mid Python Engineer, you will design and build robust backend services and APIs that power data-driven products used by real users at scale. You'll work on meaningful, complex problems in a cloud-native environment - from architecting reliable API layers to integrating Agentic AI capabilities into production systems. Our clients include Fortune 500 companies, so your work will have real visibility and measurable impact in high-stakes, global organizations. Your Responsibilities Designing and building scalable, production-grade backend services and REST APIs using Python and FastAPI. Owning your work end-to-end - from initial spec and design through deployment and monitoring. Working with PostgreSQL to model, query, and optimize data storage for reliability and performance. Containerizing and deploying services using Docker and Azure Container Apps. Managing cloud infrastructure and integrations across Azure services (Storage, Key Vault, Networking). Building and maintaining CI/CD pipelines with GitHub Actions to support continuous, reliable delivery. Integrating and developing Agentic AI solutions within backend systems. Collaborating closely with engineers, product owners, and stakeholders across disciplines - translating complex requirements into clear technical solutions. Conducting code reviews and contributing to a culture of quality, documentation, and shared ownership. Leveraging AI tools in day-to-day work to enhance productivity and output quality. What We're Looking For 2+ years of experience as a Python Engineer in a commercial environment. Strong proficiency in Python and FastAPI (or comparable async frameworks). Hands-on experience with PostgreSQL - schema design, querying, performance tuning. Solid understanding of containerization with Docker and cloud deployments on Azure (Container Apps, Storage, Key Vault, Networking). Experience with GitHub Actions or similar CI/CD tooling. Familiarity with spec-driven development - ability to work from and contribute to detailed technical specifications. Exposure to Agentic AI concepts or LLM-based systems in a production or near-production context. Strong sense of ownership and reliability - you follow through, communicate proactively, and take responsibility for outcomes. Ability to communicate clearly across disciplines - comfortable working with both technical and non-technical stakeholders. Fluent English communication, able to collaborate effectively across international teams. Nice to Have Experience with microservices or event-driven architecture. Familiarity with infrastructure-as-code tools (e.g. Bicep, Terraform). Knowledge of web application security best practices. Experience with ML pipelines or MLOps tooling. Familiarity with Azure DevOps. Here's why you'll love Cloudfide Benefits: MyBenefit platform / Multisport Enel‑Med private medical care Professional Growth: Annual 2,000 PLN development budget Access to e‑learning platforms Real opportunities to expand responsibility and lead initiatives - most of our Leads come from internal promotions As the company continuously grows, you get real opportunities to expand your responsibilities, explore new areas, and even take ownership of your own initiatives. Work Environment: Long-term cooperation: UZ or B2B (we value stable, lasting relationships) Remote-first, work from anywhere in the world (workation) Flexible hours & strong focus on work-life balance Collaboration with top engineers you can truly learn from Flat structure - every voice counts, and you help shape the company Culture & Integration: Company-wide trips (including international ones) Team integration budgets Open communication and supportive, passionate teams Equal Opportunities CLOUDFIDE is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

🏢 Summary: Opportunity for a Forward Deployed Engineer (GTM) to lead the technical side of enterprise AI platform deals, owning discovery, architecture design, and proof-of-concept delivery for production-grade agentic AI systems. The role combines backend engineering, AI/ML expertise, and customer-facing technical leadership to drive successful adoption of an AI control plane and Kubernetes-based compute platform. You will shape production AI architectures, build integrations and workflows, and influence product direction based on field feedback. 🗂️ Requirements: 3–7 years of backend software engineering experience, Production systems ownership and delivery experience, Proficiency in Python, Go, or TypeScript, Experience with Kubernetes or major cloud platforms (AWS, Azure, or GCP), Hands-on experience with LLMs, agents, evals, or ML pipelines in production, Ability to lead architecture discussions with engineering stakeholders 📃 Skills: Python, Go, TypeScript, Kubernetes, AWS, Azure, GCP, LLM, LangChain, vLLM, SGLang, Triton, MCP, ML, MLOps 🏢 Description: About TrueFoundry Every production AI system whether it's powering customer support, writing code, analyzing financial data, or diagnosing medical conditions needs the same foundational infrastructure. A way to route between models. A way to manage tools and integrate them securely. A way to orchestrate agents and enforce governance. A unified compute layer to run it all. That infrastructure layer is being built right now. We're TrueFoundry, and we're building it. We're looking for a Forward Deployed Engineer- GTM to join the team. The Problem We're Solving Companies are moving beyond simple chatbots to production agentic systems. These systems route between OpenAI, Anthropic, Google, and self-hosted models. They integrate dozens of tools via protocols like MCP. They orchestrate multi-agent workflows where agents coordinate with other agents. The infrastructure to support this doesn't exist yet. You can't just duct-tape together a few API calls and call it production-ready. You need a control plane that handles: - Intelligent routing with observability, cost policies, and fallback logic - Centralized tool and MCP server management with security and lifecycle controls - Agent orchestration with governance and guardrails - A unified compute layer to run self-hosted models, custom tools, and agents We've built two products to solve this: AI Gateway is the control plane five composable components (Prompts, LLM Gateway, MCP Gateway, Guardrails, Agent Gateway) that handle routing, orchestration, and governance. AI Deploy is the compute layer of Kubernetes-based platform that abstracts ML workloads as standard software primitives, so everything runs on unified infrastructure. We're Series A, backed by Intel Capital and Sequoia. Companies like CVS, Mastercard, Siemens, Paytm, Synopsys, and Zscaler run production AI workloads on our platform. What you'll do - Own the technical arc of the deal: discovery and the in-depth demo, the architecture discussion with the customer's engineering leaders, and the POC - all the way through to close. - Win the POC on technical merit: Co-define the success criteria with the customer, run the execution tracker and the milestones, and write the integration glue, agent workflows, and eval pipelines that prove value in their environment. The goal is delivered impact, not lines of code. - Make the architecture calls that decide how the customer would run AI in production. You are the technical decision-maker in the room - the CTO in the room - and the person their engineering leaders trust. - Be the technical face of TrueFoundry to enterprise customers, and earn the credibility that turns an evaluation into a signed contract. - Steer the product. You see, first-hand, what customers pull toward and where their first production win lands. Compile that field signal and bring structured, data-backed asks to TrueFoundry's engineering and product teams - your intuition helps shape the roadmap. Who we're looking for - Backend Engineering Experience: 3 to 7 years of backend software engineering experience with production systems you have shipped and owned. Strong in Python, Go, or TypeScript. Comfortable with Kubernetes or any major cloud (AWS, Azure, GCP). - Business Acumen: You don't just question the how but also the why - discovering impact, architecture design and deal-winning opportunities. - AI and ML literacy: You have worked with LLMs, agents, evals, or ML pipelines in production. Familiarity with the modern stack (vLLM, SGLang, LangChain, Triton, model serving) is a strong plus. - Design and Architecture: Comfortable leading an architecture discussion with a customer's engineering leaders, then doing the work yourself. High agency and a bias for action. We especially welcome founding engineers and CTOs of early-stage startups, as well as engineers from infrastructure, MLOps, and developer-tool companies. Growth path Engineering ladder: Senior FDSE to Staff or Principal FDSE, with named-account ownership and architectural authority at every level. Leadership ladder: Lead FDSE to Head of FDSE, building and running the function. Cross-functional: the role builds the breadth that leads to CTO, VP of Engineering, and founder paths. Many FDSE alumni have gone on to start their own companies.

🏢 Summary: The offer is for a Mid Backend Engineer to design and build backend services, automation workflows, and data pipeline integrations in an Azure-first, cloud-native environment. The role focuses on Python API development, CI/CD automation, Databricks pipelines, and integration with Azure services to support data-driven applications and Power BI semantic model management. It involves working on high-visibility solutions for global clients using modern data and cloud technologies. 🗂️ Requirements: 2+ years commercial experience as Backend Engineer or similar role, Proficiency in Python for backend development and automation, Experience designing and implementing REST APIs, Practical knowledge of relational and NoSQL databases, Strong SQL skills, Experience with GitHub Actions for CI/CD, Familiarity with Databricks and PySpark, Working knowledge of Azure Key Vault, Azure Repos, Azure Active Directory, Ability to integrate APIs and automate workflows in cloud environments, Fluent English for technical collaboration 📃 Skills: Python, REST, FastAPI, SQL, Databricks, PySpark, Azure, KeyVault, AzureRepos, AzureAD, GitHubActions, PowerBI, XMLA, Docker, OAuth2, NoSQL 🏢 Description: Why Join Us At Cloudfide, we engineer solutions where data truly becomes an accelerator for innovation. We build and evolve large-scale Big Data and cloud-native platforms for global clients, including leaders in retail and the Fortune 500. We process massive, complex datasets, operate fully in the cloud ecosystem, and work with the latest technologies, including AI, ML, MLOps, and modern data architectures. Our projects have measurable, real‑world impact - from enhancing customer experiences at scale to empowering data-driven decision-making in high‑visibility global organizations. As we continue to grow, you'll have the opportunity to shape solutions, mentor others, drive architecture decisions, and take ownership of strategic initiatives. Opportunity Overview As a Mid Backend Engineer , you will design and build backend services and automation workflows that power data-driven applications and Power BI semantic model management for our international clients. Your work spans backend API development, data pipeline automation via Databricks, and cloud infrastructure integration - all within a modern Azure-first, cloud-native environment . Expect high-visibility solutions used across global organizations and Fortune 500 clients, and a team that values ownership, precision, and continuous improvement. You'll join a team of passionate engineers who value technical excellence, collaboration, and continuous growth . Your Responsibilities Building and maintaining backend services for web applications using Python and REST APIs, ensuring reliability, scalability, and clean architecture. Designing and automating GitHub Actions workflows for CI/CD, release management, and deployment automation across environments. Developing Databricks pipelines using PySpark and Unity Catalog to automate data refresh flows and manage Power BI semantic models programmatically. Writing Python scripts to automate operational tasks, data processing, and integration workflows. Designing and managing relational and NoSQL databases - including logging processing statuses, tracking pipeline runs, and supporting operational monitoring. Integrating with Azure services - Key Vault for secrets management, Azure Repos for version control, and Azure Active Directory for authentication and authorization. Collaborating with data engineers, BI developers, and business stakeholders to translate requirements into robust backend and automation solutions . Following best practices in API design, security, code quality, and documentation . What We're Looking For 2+ years of commercial experience as a Backend Engineer or in a closely related engineering role. Solid proficiency in Python - scripting, automation, and backend API development. Hands-on experience with REST API design and implementation (FastAPI or similar frameworks). Practical knowledge of relational and NoSQL databases and SQL - comfortable designing schemas and writing complex queries. Experience with GitHub Actions for CI/CD, release automation, and workflow management. Familiarity with Databricks and PySpark - or strong motivation to develop these skills rapidly. Working knowledge of Azure services - particularly Key Vault, Azure Repos, and Azure Active Directory. Understanding of Power BI semantic models and programmatic interaction via REST APIs or XMLA endpoints is a plus. Strong analytical thinking, communication skills, and adaptability in dynamic, evolving project environments. Fluent English communication, able to collaborate effectively across international teams. Nice to Have Experience with Databricks Unity Catalog and data governance practices. Familiarity with Power Platform - Power Apps, Power Automate, or Dataverse. Exposure to Docker and containerized deployments . Knowledge of OAuth2 / Azure AD authentication flows for API security. Experience working in data engineering or MLOps adjacent environments. Why You’ll Love Cloudfide Benefits: MyBenefit platform / Multisport Enel‑Med private medical care Professional Growth: Annual 2,000 PLN development budget Access to e‑learning platforms Real opportunities to expand responsibility and lead initiatives - most of our Leads come from internal promotions As the company continuously grows, you get real opportunities to expand your responsibilities , explore new areas, and even take ownership of your own initiatives. Work Environment: Long-term cooperation: UZ or B2B (we value stable, lasting relationships) Remote-first , work from anywhere in the world ( workation ) Flexible hours & strong focus on work-life balance Collaboration with top engineers you can truly learn from Flat structure - every voice counts, and you help shape the company Culture & Integration: Company-wide trips (including international ones) Team integration budgets Open communication and supportive, passionate teams Equal Opportunity Cloudfide is an Equal Opportunity Employer. We value diversity and are committed to creating an inclusive environment for all employees.