MLOps / AI Operations Engineer

About the role

Industrialize AI delivery through automated deployment, evaluation operations, observability, reliability engineering and transparent consumption management.

Key responsibilities

• Build CI/CD pipelines for AI services, prompts, agent configurations, infrastructure and evaluation assets.
• Automate environment provisioning, testing, deployment, rollback and release evidence.
• Implement tracing, logging, model and agent monitoring, alerts and operational dashboards.
• Operationalize evaluation thresholds, incident handling and continuous‑improvement loops.
• Monitor latency, capacity, token usage, infrastructure consumption and cost drivers.
• Define runbooks, service ownership and production support handover.

Required profile

• 4+ years in DevOps, platform engineering, ML engineering, SRE or cloud operations.
• Strong automation, containers, cloud services, observability and Infrastructure as Code capability.
• Experience deploying or operating ML, generative AI or distributed application workloads.
• Understanding of release controls, reliability, security and cost optimization.

Required skills

• GitHub Actions, Azure DevOps, GitLab CI or equivalent.
• Infrastructure as Code using Terraform, Bicep or comparable tooling.
• Docker and Kubernetes orchestration.
• MLflow, platform‑native registries or equivalent lifecycle tooling.
• OpenTelemetry and tools such as LangSmith, Langfuse, Azure Monitor, Prometheus or Grafana.
• Progressive delivery, rollback, secrets management, vulnerability scanning, incident response and reliability practices for non‑deterministic AI systems.