Machine Learning Operations Engineer II

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 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 conferences (Amazon Re:invent, PyCon). To illustrate the point, within the same month, the same engineer went from reimplementing a prompt optimization research paper to shipping prometheus alerts. As an MLOps Engineer, you are a thoughtful, curious, collaborative, and resourceful person passionate about building and supporting a mature ML platform. 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 excel here. At Kensho, we hire talented people and give them the autonomy and support needed to build amazing technology and products. We support our employees by fostering opportunities for continual learning, pursuing their curiosities and adding to an amazing culture. We collaborate with one another in an open, honest, and efficient way to solve hard problems. We give our employees the opportunity to work from where they feel most productive and engaged (must be in the United States). We also value in‑person collaboration, so there will be times when travel to one of our Kensho hubs (Cambridge, MA or NYC) will be required for team meetings or company events. Kensho states that the anticipated base salary range for the position is 130 -175k. In addition, this role is eligible for an annual incentive bonus and equity plans. At Kensho, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. What You’ll Do: 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. What You’ll Need: 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. Technologies & Tools We Use: Development: Python, Bash, LangGraph, PyTorch Infrastructure: Ray, Amazon EKS, Airflow, Jsonnet, Terraform Ops: Git, Github, AWS, LangFuse, Sentry, Prometheus, W&B How To Really Get Our Attention: Experience with Agentic AI systems, tools, frameworks and workflows. Experience with running workflows on Ray. Experience with MCP server patterns. At Kensho, we pride ourselves on providing top‑of‑market benefits, including: 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 Recruitment Fraud Alert: If you receive an email from a spglobalind.com domain or any other regionally based domains, it is a scam and should be reported to View email address on click.appcast.io . S&P Global never requires any candidate to pay money for job applications, interviews, offer letters, “pre‑employment training” or for equipment/delivery of equipment. Stay informed and protect yourself from recruitment fraud by reviewing our guidelines, fraudulent domains, and how to report suspicious activity here ( . We are an equal opportunity employer that welcomes future Kenshins with all experiences and perspectives. Kensho is headquartered in Cambridge, MA, with an additional office location in New York City. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin. Job ID: 327217 Posted On: 2026-04-03 Location: Cambridge, Massachusetts, United States #J-18808-Ljbffr