Machine Learning Operations Engineer II

Summary

S&P Global's Kensho is a hub for AI innovation and transformation, focusing on machine learning and data discovery. The MLOps Engineer will empower ML engineers with tools and processes, ensuring robust and efficient workflows in developing AI applications.

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
• Provide resources and training for ML teams on best practices, enabling them to efficiently productionize their work to be leveraged by high-value products and services
• 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

Skills

• 2+ years of experience in ML infra, ML Ops, ML Engineering or some similar skillset
• Experience managing distributed systems with Kubernetes. It is important to understand Kubernetes concepts and trade-offs
• Cloud Platform (AWS) understanding. We utilize tools like EKS and managed ML services like Bedrock and SageMaker
• Python proficiency (we are a python shop mostly)
• Familiarity with distributed computing frameworks and workflow orchestration (ie. Ray, Airflow)
• 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
• Experience with Agentic AI systems, tools, frameworks and workflows
• Experience with running workflows on Ray
• Experience with MCP server patterns

Qualifications

Must Haves

• 2+ years of experience in ML infra, ML Ops, ML Engineering or some similar skillset
• Experience managing distributed systems with Kubernetes. It is important to understand Kubernetes concepts and trade-offs
• Cloud Platform (AWS) understanding. We utilize tools like EKS and managed ML services like Bedrock and SageMaker
• Python proficiency (we are a python shop mostly)
• Familiarity with distributed computing frameworks and workflow orchestration (ie. Ray, Airflow)
• 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

Nice to Haves

• Experience with Agentic AI systems, tools, frameworks and workflows
• Experience with running workflows on Ray
• Experience with MCP server patterns

Benefits

• Medical, Dental, and Vision insurance 100% company paid premiums
• Unlimited Paid Time Off
• 26 weeks of 100% paid Parental Leave (paternity and maternity)
• 401(k) plan with 6% employer matching
• Generous company matching on donations to non-profit charities
• Up to $20,000 tuition assistance toward degree programs, plus up to $4,000/year for ongoing professional education such as industry conferences
• Plentiful snacks, drinks, and regularly catered lunches
• Dog-friendly office (CAM office)
• Bike sharing program memberships
• Compassion leave and elder care leave
• Mentoring and additional learning opportunities
• Opportunity to expand professional network and participate in conferences and events