MLOps Engineer

Responsibilities

The MLOps Engineer will design and implement CI/CD pipelines for AI and ML model training, evaluation, and RAG system deployment, including LLMs, vectorDB, embedding and reranking models, governance and observability systems, and guardrails. They will provision and manage AI infrastructure across cloud hyperscalers (AWS/GCP) using infrastructure-as-code tools, primarily Terraform, maintain containerized environments optimized for GPU workloads, support vector database, feature store, and embedding store deployments, monitor and optimize performance, availability, and cost of AI workloads using observability tools, and collaborate with data scientists and other team members to ensure smooth transitions from experimentation to production. Additionally, they will implement security best practices, support the deployment and orchestration of agentic AI systems, and help support the deployment and orchestration of agentic AI systems (LangChain, LangGraph, CrewAI, Copilot Studio, AgentSpace, etc.).