Full-Time Senior Infrastructure Engineer - AI/ML

We are seeking a fully remote, experienced Senior Infrastructure Engineer to join our team at OpenTeams.

At OpenTeams we prioritize cloud-native, reproducible, and observable infrastructure using tools like Terraform, Helm, ArgoCD, and Kubernetes operators.

All of our infrastructure components are designed as reusable, composable building blocks that support AI/ML workflows including model training, inference serving, experiment tracking, and data processing pipelines using tools from the PyData ecosystem.

In this position, you'll get to:

• Significantly contribute to the evolution of Nebari and design reusable, modular infrastructure components that can be composed into bespoke Kubernetes-based platforms for sovereign AI deployments
• Develop composable MLOps components and infrastructure patterns supporting model training, serving, monitoring, and CI/CD pipelines that organizations can own and operate
• Design and implement observability, monitoring, and cost optimization strategies for large-scale AI/ML workloads on client-owned Kubernetes infrastructure
• Collaborate with ML engineers to optimize infrastructure for training ML models, quantizing and packaging open weight LLMs, computer vision workloads, and other AI applications in sovereign environments
• Contribute to open-source MLOps tooling and Kubernetes ecosystem projects that enable data sovereignty
• Work with clients to deploy, configure, and optimize their sovereign AI infrastructure
• Collaborate with a fully remote distributed team using asynchronous communication methods

What We're Looking For:

• 4+ years of hands-on infrastructure/platform/DevOps experience with production systems
• Strong understanding of infrastructure engineering principles: scalability, reliability, observability, and automation
• Solid experience with Kubernetes in production environments, including troubleshooting and optimization
• Proficiency with Infrastructure-as-Code tooling (Terraform, Helm, or similar) for managing complex deployments
• Experience with at least one major cloud platform (AWS, Azure, GCP) including networking, security, and compute services
• Strong programming skills, particularly in Python and/or Go, with ability to write maintainable infrastructure code
• Experience contributing to technical initiatives or mentoring junior team members
• Understanding of CI/CD practices, GitOps workflows, and infrastructure automation principles

Bonus points for experience with:

• MLOps pipelines and ML infrastructure (model training, serving, monitoring)
• Multiple cloud platforms and their AI/ML services
• On-premises deployment and hybrid cloud environments
• ML/AI ecosystem tools (PyTorch, TensorFlow, scikit-learn, etc.)
• Monitoring and observability tools (Prometheus, Grafana, distributed tracing)
• Data sovereignty, privacy, and security requirements for enterprise AI
• GPU infrastructure and model serving frameworks (KServe, vLLM, LLM-D)
• ML workflow orchestration tools (Kubeflow, MLflow, Airflow, Prefect)
• Service mesh technologies (Istio, Linkerd) and advanced Kubernetes networking
• Open-source contributions to Kubernetes, MLOps, or AI infrastructure projects
• Cost optimization and resource management for ML workloads
• Air-gapped or highly secure deployment environments

What is the last date for applying to the job?

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