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Kensho is S&P Global’s hub for AI innovation and transformation. With expertise in machine learning, natural language processing, and data discovery, we develop and deploy novel solutions to innovate and drive progress at S&P Global and its customers worldwide. Kensho's solutions and research focus on business and financial generative AI applications, agents, data retrieval APIs, data extraction, and much more. The MLOps team is the de facto ML platform team at Kensho. Our team’s mission is critical: empower our ML engineers with state-of-the-art processes, tooling, and infrastructure to iterate quickly, build reliably, and identify potential production issues early.
Job Responsibility:
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
Requirements:
2+ years of experience in ML infra, ML Ops, ML Engineering or some similar skillset
Experience managing distributed systems with Kubernetes
Cloud Platform (AWS) understanding
Python proficiency
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
Nice to have:
Experience with Agentic AI systems, tools, frameworks and workflows
Experience with running workflows on Ray
Experience with MCP server patterns
What we offer:
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