MLOps / AI Platform Engineer

Operationalizes AI: deployment, monitoring, cost, and governance for models and LLM applications running in production.

What you'll do

• Build CI/CD pipelines for model and LLM application deployment
• Stand up model monitoring, versioning, and rollback systems
• Manage cost, latency, and reliability of production AI workloads
• Implement governance controls: access management, audit logging, usage tracking
• Partner with the security team on AI-specific compliance requirements
• Support multi-cloud AI infrastructure across AWS, Azure, and GCP

Skills

What you bring

• 4+ years in DevOps or platform engineering, including 1–2+ years specific to ML/AI systems
• Experience with Kubernetes, Docker, and Terraform or an equivalent infrastructure-as-code tool
• Familiarity with MLOps tooling such as MLflow, Kubeflow, SageMaker, or Vertex AI
• Strong scripting ability in Python or Go, with CI/CD pipeline experience
• Working understanding of model versioning, monitoring, and cost optimization practices

Nice to have

• Experience with LLM-specific observability tools such as LangSmith or Arize
• AWS, Azure, or GCP cloud certification