Sr. Site Reliability Engineer (SRE)

🏢 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: 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 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: 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: 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: Senior Site Reliability Engineer role focused on building and standardizing SRE practices across a hybrid AWS and on-prem infrastructure. The position centers on ensuring scalability, resilience, and high availability of high-frequency, data-intensive platforms through observability, automation, and Kubernetes optimization. You will define SLOs, enhance monitoring architecture, and drive reliability culture across engineering teams. 🗂️ Requirements: 5+ years experience in SRE, DevOps, or Infrastructure Engineering supporting distributed production systems, Bachelor’s degree in Computer Science, Computer Engineering, or related field (or equivalent experience), Deep expertise in Grafana, Prometheus, Loki, and Tempo (OpenTelemetry), Strong production experience with Docker and Kubernetes, Experience managing hybrid infrastructure (AWS and on-premises), Proficiency in at least one language: Python, Go, or Bash, Hands-on experience with CI/CD pipelines and Infrastructure-as-Code, Experience defining and managing SLOs and SLAs, Willingness to participate in on-call rotation 📃 Skills: AWS, Kubernetes, Docker, Prometheus, Grafana, Loki, Tempo, OpenTelemetry, Python, Go, Bash, CI/CD, IaC, Git, Hypervisors 🏢 Description: About the Client Our client is a premier, global investment management firm operating at the intersection of finance and technology. Known for their sophisticated, data-intensive systems, they build and maintain high-performance platforms that process massive volumes of market and operational data. To support their expanding footprint, they are looking for a senior-level Site Reliability Engineer (SRE) who will take ownership of shaping, standardizing, and scaling their SRE frameworks and reliability culture from the ground up. The Role In this role, you will serve as a foundational force for SRE practices, partnering directly with Cloud, Infrastructure, and Software Engineering squads. You will work across a hybrid infrastructure (combining advanced AWS cloud environments and physical on-premises servers) to guarantee the scalability, resilience, and maximum uptime of critical, high-frequency transactional platforms. Core Responsibilities SRE Evangelism: Design, implement, and champion core reliability principles, helping technology teams adopt sustainable scaling practices. Observability Architecture: Implement, scale, and maintain end-to-end monitoring, telemetry, and distributed tracing systems utilizing Prometheus, Grafana, Loki, and Tempo (OpenTelemetry framework). Kubernetes Optimization: Establish best-practice configurations for containerized workloads, ensuring applications running on Kubernetes are highly resilient, cost-effective, and performant. Incident Management & Culture: Participate in a balanced, shared on-call rotation (averaging one week per month). Automation & Engineering: Build custom tooling and CI/CD pipelines to automate routine tasks, system health checks, and rapid disaster recovery workflows. SLO/SLA Definition: Partner with product and engineering teams to define, monitor, and enforce Service Level Objectives (SLOs) and Error Budgets. What We Look For Experience: 5+ years of hands-on experience in a dedicated SRE, DevOps, or Infrastructure Engineering role supporting complex, distributed production systems. Education: A Bachelor’s degree in Computer Science, Computer Engineering, or a related technical discipline (or equivalent practical experience). Observability Expertise: Deep, subject-matter knowledge of modern monitoring stacks, specifically Grafana, Prometheus, Loki, and Tempo (OTel). Orchestration & Containers: Strong, production-grade expertise in containerization (Docker) and orchestration (Kubernetes). Hybrid Infrastructure: Experience navigating hybrid models—managing both cloud services (AWS preferred) and physical on-premise hardware resources. Scripting/Coding: Proficiency in writing clean, maintainable code in at least one scripting or programming language (e.g., Python, Bash, or Go) to build reliable automation. Methodologies: Solid grounding in CI/CD concepts, infrastructure-as-code (IaC), and agile development processes. Soft Skills: Excellent verbal and written communication skills, with a proven ability to convey complex infrastructure and reliability concepts to both technical and non-technical stakeholders. What We Offer Stable Employment: Full-time employment contract ( Umowa o Pracę - UoP ). Tax Optimization: Eligibility for creative tax-deductible costs ( KUP - Koszty Uzyskania Przychodu). Financial Reward: Highly competitive base salary accompanied by a generous annual performance bonus . Comprehensive Health: Premium private medical care package that fully includes dental coverage (stomatologia) . Wellness & Lifestyle: MultiSport card to keep you active and healthy. Daily Perks: Pre-funded lunch card for your daily meals. Tech Stack at a Glance Cloud & Virtualization: AWS, Kubernetes, Docker, On-Premises Hypervisors Observability: Prometheus, Grafana, Loki, Tempo, OpenTelemetry (OTel) Languages: Python, Go, Bash CI/CD & Automation: Git-based pipelines, Configuration Management, IaC

🏢 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: Permanent role for an experienced OpenShift/Kubernetes Engineer to design, build, and operate secure OpenShift clusters in highly regulated, disconnected environments. The position focuses on reliability, automation, security, and scalable container infrastructure, including cluster lifecycle management and application onboarding. You will work end-to-end on platform operations, automation, and compliance within an Agile environment with on-call responsibilities. 🗂️ Requirements: Production experience running OpenShift clusters, Hands-on cluster installation, upgrades, patching, Operator lifecycle management, Strong Kubernetes/OpenShift networking knowledge, Experience with NetworkPolicies and traffic controls, Automation using Helm and Ansible, Practical application of GitOps principles, Experience in disconnected/restricted environments, Content and image mirroring (oc-mirror), Proficiency with git, YAML, oc/kubectl, bash, Availability for 24/7 on-call rotation 📃 Skills: OpenShift, Kubernetes, Helm, Ansible, GitOps, YAML, Bash, Git, oc, kubectl, Operators, NetworkPolicies, oc-mirror, VSCode 🏢 Description: About the role: We’re looking for an experienced OpenShift/Kubernetes Engineer to join a focused platform team that designs, builds, and operates OpenShift clusters in a highly secure disconnected environment. You’ll own reliability, automation, and security end‑to‑end—partnering with squads to onboard applications, harden the platform, and scale our container infrastructure for regulated workloads. Responsibilities: Operate and maintain OpenShift clusters to ensure availability, scalability, and performance (including patching, upgrades, and capacity planning). Automate cluster installation and configuration using GitOps, Ansible, Helm, and Operators; codify repeatable workflows and golden patterns. Implement platform security best practices and compliance controls; run vulnerability assessments and remediation in line with organizational policies Contribute to new cluster and network design, including multi‑tenant patterns and service connectivity. Support onboarding of new client applications and provide ongoing platform guidance and troubleshooting. Build and maintain clear, living documentation for operations runbooks, standards, and reference architectures. Deliver within Agile/Scrum ceremonies; collaborate closely with developers, SREs, and security. Work effectively in highly secure, disconnected environments, including image and content mirroring. Provide 24/7 on‑call support as part of a rotating team shift schedule, including occasional weekend coverage Required experience & skills: Experience: a few years running OpenShift clusters in production, ideally in banking or similarly regulated environments. Core OpenShift skills: cluster installation, upgrades, patching, and Operator lifecycle management. Networking: Understanding of Kubernetes/OpenShift networking concepts, including NetworkPolicies and traffic controls.. Automation: Strong working knowledge of Helm and Ansible, with experience applying GitOps principles to manage configuration and lifecycle operations. Disconnected operations: Experience operating in restricted environments, including content and image mirroring (e.g. oc‑mirror). Everyday tooling: Comfortable with git, YAML, oc/kubectl, bash, and Visual Studio Code. Nice to have / Plus skills Observability: Working knowledge of Prometheus and Grafana for monitoring, alerting, and operational troubleshooting. Service Mesh: Red Hat Service Mesh (Istio) fundamentals and operations. Container tooling: such as Podman and Skopeo for build, debug, and image management. Infrastructure as Code beyond core automation (e.g. Terraform). CI/CD: Working with pipelines (Azure DevOps). Experience in working as a power user or system administrator. Some basic knowledge in virtualization and HCI: VMware, Nutanix/AHV. Observability & ITOM: Splunk; ServiceNow and IBM Netcool/Omnibus. Artifact management: JFrog Artifactory. How you work Pragmatic SRE mindset with a bias to automate, document, and standardize. Comfortable pairing with developers and security to meet compliance without slowing down delivery. Thrives in Agile/Scrum, communicates clearly, and owns outcomes end‑to‑end. Please note that this is a permanent position, and we do not offer freelance/contract arrangement for the role. #LI-AK1 About Us Why join us Embark on your new adventure at Euroclear, and work at the heart of the global capital markets. We connect over 2,000 financial institutions across the globe. As an open and resilient infrastructure, we contribute to the stability of the financial markets. We help clients cut through complexity, lower costs, and mitigate risks of financial transactions. At Euroclear, we have the clear ambition to use our key role to facilitate and accelerate a sustainable global financial system. What We Offer: Work closely with inspiring, supportive and engaged colleagues from more than 80 different countries. Practice your talents in a highly professional international environment. Join a learning and development environment with an emphasis on knowledge sharing and training. Competitive salary and comprehensive benefits. Ways of working Find your own optimal balance within our hybrid working model, where you can connect at the office 8 days a month and also benefit from remote working. Great Place to Work for All We are committed to creating an inclusive culture that celebrates diversity and strives to be a Great Place to Work for All. All qualified applicants will be considered for employment, regardless of any aspect that makes them unique (including race, religion, national origin, gender, sexual orientation, age, marital status, pregnancy, disability, ...). If you need any specific accommodation due to disability or any other reason, you can let the recruiter know during your application process. Our values guide how we work together and shape our future: Our mission and values - Euroclear About the Team Group Technology Services (GTS) plans, builds and runs the global infrastructure of Euroclear, and is additionally responsible for the IT service management to the operating entities of the various Group markets. Group Technology Services operates its IT systems and IT services in such a way that, at an appropriate and benchmarked cost level, it can deliver the agreed levels of service to support the requirements of the business processes, for both normal operations and during disaster recovery.

🏢 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.