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Required Skills:

Must have at least 5 years in working in a MLOps, DevOps and/or Cloud Engineering role where at least for the last several years focus has been on AI or ML systems

Experience building AI cost tracking and observability frameworks across cloud platforms such as Azure, Google and Snowflake

Strong experience with Azure ML, Google Vertex AI/Gemini or OpenAI Service

Experience working with Datadog or equivalent observability tools that alert for latency and drift

Must have advanced Python and SQL skills including stored procedures

Experience integrating cost and performance monitoring into CI/CD pipelines

Understanding of FinOps principles, cloud billing APIs, and cost optimization

Desired Skills:

Experience implementing GenAI/Agentic AI frameworks such as LangChain or RAG pipelines

Experience or knowledge of cost tracking best practices in regulated industries such as ISO, SOC2 or HITRUST

Description of Role/Responsibilities/Project:

We are seeking a MLOps Engineer to work on building greenfield cost tracking and observability frameworks using AI across multi-cloud technologies such as Azure, Google and Snowflake. This person should have experience with tools such as Datadog where they have designed and built dashboards that collaborate across DevOps and FinOps to model cost visualization and optimization. This person will work directly with the Chief Data Officer and they play a key role in making sure the ML and AI produce accurate data that the CDO will then use to make critical business decisions.

This is a high-impact, visible role where you'll directly influence how AI systems are monitored, optimized, and delivered across the enterprise. In this role, you'll design and implement AI cost tracking and observability frameworks spanning Azure, Google Cloud, and Snowflake, ensuring our AI and ML workloads are both high-performing and cost-efficient. You'll collaborate with cross-functional teams data scientists, platform engineers, and FinOps to embed cost and performance monitoring into CI/CD pipelines, and develop automation that helps scale AI operations intelligently.

This is an excellent opportunity for a forward-thinking engineer who wants to work with emerging AI technologies, collaborate with top technical talent, and help shape the next generation of AI infrastructure in a dynamic, growth-focused environment.

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