Senior Software Engineer, Compute Platform

🏢 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: Engineering role focused on building a unified infrastructure platform that bridges bare-metal servers, GPU clusters, storage, and networking into a programmable system for large-scale AI and HPC workloads. The position involves designing orchestration layers, automation frameworks, APIs, and observability systems to manage compute, storage, and networking at scale. The goal is to deliver high-performance, compliant, and enterprise-grade AI infrastructure for demanding research and production environments. 🗂️ Requirements: 5+ years software engineering experience in infrastructure platforms, distributed systems, or cloud platforms, Strong Kubernetes knowledge and container orchestration experience, Deep understanding of compute, storage, and networking infrastructure, Experience with systems programming languages (Go, C/C++, Rust, Python), Strong Linux production experience, Knowledge of bare-metal provisioning and virtualization technologies, Experience designing APIs, SDKs, and automation frameworks, Familiarity with AWS, GCP, or Azure, Experience with Infrastructure-as-Code tools, Experience with network automation and high-performance networking concepts, Understanding of observability, telemetry, and logging systems, Knowledge of enterprise security and compliance standards (SOC2, ISO27001) 📃 Skills: Go, Python, Kubernetes, Docker, Terraform, Ansible, Linux, KVM, SLURM, AWS, GCP, Azure, BGP, VXLAN, InfiniBand, RDMA, SR-IOV, FastAPI, PostgreSQL, Redis, Prometheus, Grafana, ELK, NVIDIA 🏢 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 the comprehensive infrastructure platform that bridges our physical infrastructure – bare-metal servers, GPU clusters, high-performance storage, and networking fabric – with the systems our customers depend on for large-scale computation, inference, simulations, and training. Working closely with product, your platform team members, and infrastructure specialists, you'll design and implement the orchestration layer, APIs, and automation framework that make thousands of servers, petabytes of storage, and high-speed networks feel like a unified, programmable platform. Job Responsibilities Infrastructure Abstraction Layer: Design and build systems that bridge physical infrastructure (bare-metal servers, storage clusters, network fabric) with customer-facing services, enabling programmatic management of compute, networking, and storage at scale. 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. Platform Orchestration: Implement comprehensive orchestration systems that coordinate across compute, storage and networking to deliver unified experience for complex research workloads. Network Automation & Placement: Design and build network provisioning automation including intelligent VM placement decisions for optimal network topology, automated VLAN and subnet configuration, and software-designed networking orchestration for high-performance interconnects. Enterprise APIs & SDKs: Develop robust APIs and SDKs that enable researchers and engineering teams to programmatically provision and manage infrastructure resources across all platform domains. Observability & Telemetry: Implement comprehensive observability, telemetry, and logging systems that provide visibility into infrastructure health, performance, and utilization across the infrastructure footprint. Performance Engineering: Build and optimize platform services that deliver consistent high-throughput low-latency networking for demand research applications and data-intensive workloads. Cross-Team Collaboration: Work closely with engineering, infrastructure, and product to define requirements, drive infrastructure-product-rollouts, and improve resource lifecycle management. Compliance & Security: Implement platform-wide compliance and security features supporting SOC 2, ISO 27001, and enterprise regulatory requirements including comprehensive audit logging, access controls, and data residency management. Requirements Experience: 5+ years in software engineering with a proven track record of infrastructure platforms, distributed systems, or cloud platforms for production environments. 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. Infrastructure Systems: Strong understanding of infrastructure fundamentals including compute orchestration, storage systems, networking technologies, and how they integrate together to deliver complete platform experiences. Programming Skills: Experience with systems programming languages (Go, C/C++, Rust, Python) for performance-critical components is a strong plus. Linux Production Experience: Strong experience with linux in production environments, including systems administration, performance tuning, and troubleshooting. Bare-Metal & Virtualization: Deep knowledge of bare-metal infrastructure, provisioning systems, out-of-band management, and virtualization technologies (KVM, Kubernetes, etc). API & Platform Design: Proven experience designing and building APIs, SDKs, and automation frameworks that enable programmatic infrastructure management. Cloud Platform Knowledge: Strong familiarity with cloud environments (AWS, GCP, Azure) and understanding of how to translate cloud-native patterns to bare-metal infrastructure. Infrastructure Automation: Experience with Infrastructure-as-code tools (Terraform, Ansible) and building automated deployment pipelines. Problem Solving & Autonomy: Self-starter who can navigate ambiguity, balance pragmatic shipping with good long-term architecture, and independently drive complex technical initiatives. Communication Skills: Strong written and verbal communication skills, including ability to write clear technical communication and collaborate across teams. 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 building internal platforms, infrastructure-as-a-service, or developer tooling Background with GPU computing platforms and AI/ML infrastructure requirements Knowledge of high-performance networking technologies (InfiniBand, RDMA, SR-IOV) Experience with observability and monitoring platforms (Prometheus, Grafana, ELK stack) Familiarity with both cloud-native and bare-metal infrastructure deployment models Understanding of enterprise compliance requirements and security best practices Extra points for experience with financial services technology infrastructure and understanding of trading system requirements Key Technologies Go, Python, Kubernetes, Docker, Terraform, Ansible, Linux, Networking (BGP, VXLAN), Storage Systems, FastAPI, PostgreSQL, Redis, NVIDIA GPU Technologies, InfiniBand 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: High-impact compute infrastructure role focused on building and optimizing one of the world’s largest AI supercomputers for large-scale training and inference workloads. The position involves low-level GPU optimization, Linux kernel and virtualization work, distributed systems, and orchestration across massive GPU clusters. Engineers will collaborate closely with AI research teams to maximize performance, scalability, and reliability. 🗂️ Requirements: Deep systems programming experience in C/C++ or Rust, Experience with large-scale GPU clusters or distributed compute infrastructure, Hands-on GPU kernel optimization experience, Knowledge of Linux kernel internals, scheduling, virtualization, or orchestration, Experience building high-performance AI infrastructure for training or inference, Ability to optimize memory-bound and compute-bound workloads, Experience with exabyte-scale storage systems 📃 Skills: CUDA, CUTLASS, Tensor, Nsight, Linux, KVM, Firecracker, Kubernetes, C++, Rust, GPU, CUDA, GeMM, Attention 🏢 Description: SpaceXAI's mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company's mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates.ABOUT THE ROLE: We are building one of the world's largest AI supercomputers from the ground up. As part of the Compute Infrastructure team, you will own both the raw GPU supercomputer and the platform layer that runs on top of it. You will work across the full stack — from low-level GPU kernel optimizations and Linux kernel internals to massive-scale orchestration and virtualization — to make training and inference at SpaceXAI as fast, reliable, and scalable as possible. This is a broad, high-impact role that combines hardcore supercompute and compute infrastructure work. Your contributions will directly accelerate Grok's training speed and overall AI progress. RESPONSIBILITIES: Design, build, and optimize massive GPU clusters for extreme-scale training and inference workloads Develop and tune low-level CUDA kernels (GeMM, Attention, etc.), using CUTLASS, Tensor Cores, and Nsight for maximum performance Profile, debug, and eliminate bottlenecks across GPU memory hierarchy, networking fabric, filesystems, and multi-GPU operation Collaborate closely with AI research teams to deliver production-grade performance and scalability PREFERRED SKILLS AND EXPERIENCE: Deep low-level systems programming (C/C++/PTX/SASS) Strong experience with large-scale GPU clusters or distributed compute infrastructure at production scale Hands-on work with GPU kernel optimization (CUTLASS, custom kernels, Nsight profiling) Track record of building or running high-performance infrastructure for AI workloads (training or inference platforms) Ability to reason from first principles and optimize for both memory-bound and compute-bound scenarios COMPENSATION AND BENEFITS: $180,000 - $440,000 USD Base salary is just one part of our total rewards package at SpaceXAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.SpaceXAI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.

🏢 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: 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 backend-focused AI Engineer role building a modern AI platform for business process automation and multi-agent system integration in a production-grade enterprise environment. The position centers on developing scalable Python-based services, secure API integrations, and cloud-native AI solutions using LLMs and RAG. The role involves designing robust, secure, and scalable architectures deployed on AWS or Azure with containerized infrastructure. 🗂️ Requirements: Extensive experience in backend development with Python in production environments, Hands-on experience with AI Agents, RAG, MCP, or LLM-based solutions, Experience with system integrations and API architecture, Experience with scalable cloud-based applications, Experience with AWS or Azure, Experience with Docker and Kubernetes, Experience designing secure and scalable enterprise solutions, Experience with multi-agent systems and workflow automation, Ability to conduct code reviews and mentor developers, Fluent English and Polish 📃 Skills: Python, LLM, RAG, MCP, AWS, Azure, Docker, Kubernetes, API, Microservices, Cloud, AI, Agents 🏢 Description: Additional information The project focuses on building and developing a modern AI platform supporting business process automation and the integration of intelligent agents with enterprise systems. The team is creating scalable solutions based on Python, cloud technologies, and multi-agent architecture, leveraging technologies such as LLMs, RAG, and advanced workflow automation. A key aspect of the project is designing secure integrations between AI platforms, internal systems, and external APIs within a production-grade environment. The project is delivered in an international technology-driven environment with a strong focus on innovation, high-quality architecture, and the development of cutting-edge AI solutions for a global industrial organization. You’re ideal for this role if you: Have extensive experience in backend development with Python in production environments Have hands-on experience with AI technologies such as AI Agents, RAG, MCP, or LLM-based solutions Understand system integrations, API architecture, and scalable cloud-based applications Have worked with AWS or Azure and containerized environments such as Docker and Kubernetes Are experienced in designing secure and scalable enterprise solutions Have strong problem-solving skills and can proactively remove technical blockers Enjoy working closely with cross-functional teams in an international environment Have experience mentoring developers, conducting code reviews, and promoting engineering best practices Are interested in modern AI architectures, workflow automation, and multi-agent systems Communicate fluently in English and Polish and feel comfortable working in a global setup Your day-to-day responsibilities include:​ Designing and developing scalable backend services and AI-driven integration solutions in Python Building and maintaining APIs, connectors, and middleware between AI platforms and enterprise systems Creating and optimizing multi-agent workflows and intelligent automation processes Collaborating with AI Engineers, Data Engineers, and other technical stakeholders on system architecture and integrations Integrating external AI platforms, internal tools, and cloud services into production environments Working with cloud technologies and containerized infrastructure in AWS or Azure environments Improving the quality, reliability, and performance of AI-powered solutions and LLM-based workflows Conducting code reviews, mentoring team members, and supporting engineering best practices Troubleshooting technical issues, identifying bottlenecks, and driving continuous improvements Contributing to technical documentation, architecture discussions, and innovation initiatives across the team

🏢 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.