Machine Learning Operations Engineer II

Job title: Machine Learning Operations Engineer II

About the Role Kensho, S&P Global’s hub for AI innovation and transformation, develops and deploys ML-based solutions across the organization. The MLOps team is the de facto ML platform team responsible for enabling ML engineers with state-of-the-art processes, tooling, and infrastructure to iterate rapidly and produce production-ready models and agents. As an MLOps Engineer, you’ll help build a mature, scalable agentic platform and work across multiple teams to improve developer experience and reduce engineering toil.

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 processes, identify pain points, and form effective solutions
• Empower engineers with stable tooling to rapidly experiment and turn research into demonstrable prototypes and mature products
• Provide resources and training for ML teams on best practices, enabling efficient productionization for high-value products and services
• Evaluate, select, and champion open source and third-party solutions, driving adoption and integration into Kensho’s platform
• Ship scalable, automated processes for model fine-tuning, reinforcement learning, and evaluation of LLMs/Agents
• Improve LLM and Agentic observability to monitor production deployments, detecting performance, decay, and drift issues
• Stay at the frontier by tracking emerging tools/frameworks, promoting best practices, and strengthening the team’s technical depth

What We're Looking For

• 2+ years of experience in ML infra, ML Ops, ML Engineering, or related
• Experience managing distributed systems with Kubernetes and understanding Kubernetes concepts and trade-offs
• Cloud Platform (AWS) understanding; experience with EKS and managed ML services like Bedrock and SageMaker
• Python proficiency (we are a Python shop)
• Familiarity with distributed computing frameworks and workflow orchestration (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
• Curious, driven, low-ego, and eager to learn across engineering disciplines
• Technologies & Tools We Use: Python, Bash, LangGraph, PyTorch, etc.

Nice to Have

• Experience building agentic applications and contributing to open-source tools
• Familiarity with monitoring/observability tooling (e.g., Prometheus)
• Comfortable working across multiple teams with diverse product workflows

Compensation & Benefits

• Base salary range: 130,000 – 175,000 USD per year
• Eligible for annual incentive bonus and equity plans
• Kensho emphasizes autonomy, continual learning, collaborative culture, and opportunities to contribute to cutting-edge AI research