Build and operate the AI platform infrastructure enabling developers to ship LLM-based services faster.
Implement and maintain Kubernetes-based runtime environments (incl. AKS ) for AI workloads.
Manage infrastructure as code with Terraform (modules, environments, CI/CD automation).
Support LLM workflows : RAG, agents, prompt experimentation, evaluations, and deployment patterns.
Integrate and operate tooling such as Azure AI Foundry , LiteLLM , Langfuse , MLflow .
Orchestrate pipelines using Kubeflow Pipelines and/or Argo Workflows (build, deploy, evaluate).
Improve platform reliability and observability (monitoring, logging, tracing, cost/perf signals).
Collaborate closely with developers to streamline DX (APIs, templates, docs, golden paths, automation).

Strong hands-on experience with Kubernetes in production (preferably AKS).
Solid Terraform expertise (IaC best practices, multi-env setups).
Practical experience supporting ML/LLM workloads in a platform or DevOps/MLOps context.
Proficiency in Python for automation, scripting, and supporting APIs/evaluation tooling.
Understanding of CI/CD , release processes, and production-grade operations.
Ability to work under tight timelines and deliver pragmatically.
Nice to Have: Experience building internal developer platforms or “paved roads” for engineering teams.
Familiarity with LLM evaluation frameworks , prompt testing workflows, and LLM observability.
Exposure to RAG architectures , vector databases, and agentic patterns.
Experience with Kubeflow , Argo , and ML lifecycle tooling.

MLOps Engineer

Key skills