Mlops Engineer

Job Title

Build a common AI cost tracking and observability framework across Azure, Google, and Snowflake. Integrate billing and usage APIs (Azure ML, OpenAI, Gemini/Vertex AI). Develop model-level metadata tagging for cost attribution and trend analysis. Implement and manage Datadog dashboards and alerting for latency, drift, and anomalies. Collaborate with DevOps and FinOps on model cost visualization and optimization. Deliver knowledge transfer and documentation to internal teams at project close.

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Skills and Requirements

• 5+ years in MLOps/DevOps/Cloud Engineering focused on AI or ML systems
• Strong experience with Azure ML, Google Vertex AI/Gemini, OpenAI Service
• Expertise in Datadog or equivalent observability tools
• Advanced Python and SQL skills for automation and analytics
• Experience integrating cost and performance monitoring into CI/CD pipelines
• Understanding of FinOps principles, cloud billing APIs, and cost optimization
• Experience with GenAI/Agentic AI frameworks (LangChain, RAG pipelines) and cost tracking in regulated environments (ISO, SOC2, HITRUST)