Machine Learning Operations Engineer I

Kensho, S&P Global’s AI innovation hub, is hiring an MLOps Engineer I to build and support the ML platform used across its AI teams. The role focuses on developing reliable ML workflows and tooling, supporting model and agent production, improving observability, and helping engineers adopt effective tools and practices.

Responsibilities:

• Iterate on Kensho’s ML processes to develop tools, services, and frameworks that make every stage of the ML workflow robust, auditable, and usable
• Work closely with ML engineers to understand their unique processes, identify pain points, and form effective solutions
• Empower engineers with the stable tooling necessary to rapidly experiment and actualize their research into demonstrable prototypes and mature products
• Evaluate, select and champion open source and third-party solutions, driving their adoption across teams and integrating into Kensho’s existing platform ecosystem
• Ship scalable, efficient, and automated processes for model fine-tuning and reinforcement learning and for the evaluation of LLMs/Agents
• Improve LLM and Agentic observability to help monitor agentic applications in production, detecting performance, decay and drift issues
• Stay at the frontier by actively tracking emerging tools and frameworks, promote best practices and strengthen the technical expertise of the team with your unique skill set

Requirements:

• 1+ years of experience in ML infra, ML Ops, ML Engineering or some similar skillset
• Experience with Kubernetes
• Cloud Platform (AWS) understanding
• Python proficiency (we are a Python shop mostly)
• Familiarity with software engineering best practices in an ML context
• Some basic understanding of ML concepts, LLMs and agents
• Ability to debug distributed systems across infrastructure, networking and application layers
• Excellent communication skills to drive adoption of new tools and best practices across multiple teams
• Someone who's very curious, driven, low-ego and eager to learn across a range of engineering disciplines, while being part of a fantastic team
• Familiarity with distributed computing frameworks and workflow orchestration (ie. Ray, Airflow) is a big plus
• Agentic AI systems, tools, frameworks and workflows
• MCP server patterns
• Eval pipelines