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
Experience building internal developer platforms or "paved roads" for engineering teams (Nice to Have)
Familiarity with LLM evaluation frameworks , prompt testing workflows, and LLM observability (Nice to Have)
Exposure to RAG architectures , vector databases, and agentic patterns (Nice to Have)
Experience with Kubeflow , Argo , and ML lifecycle tooling (Nice to Have)

MLOps Engineer

Key skills