Machine Learning Operations Engineer II
AI summary of the role
Kensho, S&P Global's AI innovation hub, seeks an MLOps Engineer to join its small, high-leverage ML platform team.
No longer in the current catalog. Last included 2026-09-09. Check the employer’s posting for availability.
What you’ll do
- Iterate on ML processes to develop tools, services, and frameworks for robust, auditable, and usable ML workflows.
- Work closely with ML engineers to understand processes, identify pain points, and form effective solutions.
- Ship scalable, efficient, and automated processes for model fine-tuning, reinforcement learning, and evaluation of LLMs/Agents.
- Improve LLM and Agentic observability to monitor agentic applications in production, detecting performance, decay and drift issues.
What you’ll bring
- 2+ years of experience in ML infra, ML Ops, or ML Engineering.
- Experience managing distributed systems with Kubernetes.
- Cloud Platform (AWS) understanding, including EKS and managed ML services like Bedrock and SageMaker.
- Python proficiency.
Technologies
Kubernetes · AWS · EKS · Bedrock · SageMaker · Python · Ray · Airflow · LangGraph · PyTorch · Terraform · Prometheus
About Kensho
Builds AI retrieval, extraction, entity-linking, and transcription products that make S&P Global data usable inside financial workflows and agentic applications.
Acquired · 100–200 people
Source and classification
Internal deployment & tooling · Evidence for this classification:
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. We sit at the intersection of infrastructure and ML, and work closely with all our ML teams (ML Product teams, R&D, …) and our infrastructure teams (Core Infra, SRE, Security). We are a small and high-leverage team: our work practically touches every AI project at Kensho. We balance pragmatic platform development with hands-on exploration at the frontier: building agentic applications ourselves, contributing to open-source tools, and defining what a mature agentic platform looks like before the industry has settled on the answers. You’re equally likely to find us at a top ML conference (NeurIPS, ICLR, ICML) and at major software and infra
More from the job description
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. At Kensho, we hire talented people and give them the autonomy and support needed to build amazing technology and products. We collaborate using our teammates' diverse perspectives to solve hard problems. Our communication with one another is open, honest, and efficient. We dedicate time and resources to explore new ideas, but always rooted in engineering best practices. As a result, we can innovate rapidly to produce technology that is scalable, robust, and useful. 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. We sit at the intersection of infrastructure and ML, and work closely with all our ML teams (ML Product teams, R&D, …) and our infrastructure teams (Core Infra, SRE, Security). We are a small and high-leverage team: our work practically touches every A [... source excerpt omitted ...] orm. You are not afraid to dig deep in both infrastructure and ML topics. You’re excited to work on internal tooling enabling ML engineers to iterate faster and build high-quality production-ready models, agents, and products. You love improving the developer experience (including your own!) and find genuine satisfaction in making engineers more effective, whether by saving engineering hours or amplifying the impact of an engineering organization. You take pride in having a multiplier effect across an engineering team or process, and you enjoy working with multiple teams with different products and workflows. Excited by what you’ve read so far? If so, we would love to help you exc [... source excerpt omitted ...] periment 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 i
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