About The Role
As a Machine Learning Engineering Manager, you will lead a team of ML Engineers and Applied ML Scientists developing Kenshos GenAI platform, LLM-powered applications, and foundational AI toolkits like Kensho or . You will guide the team in transforming advanced ML research into reliable, scalable, and production-ready systems used across S&P Global.
Your responsibilities span deep technical leadership, people management, and cross-functional collaboration. You will ensure your team is productive, supported, and delivering high-impact ML systems that align with product and business goals. While your primary focus is enabling your teams success, you will remain close enough to the technical work to make informed decisions, mentor effectively, and contribute where your expertise adds value.
What we are looking for-
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Have 7+ years of industry experience designing, building, evaluating, and maintaining robust and scalable production ML systems
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Have 2+ years of experience managing ML engineering or applied ML teams
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Have experience mentoring engineers and scientists, with a long-term mindset toward team development and hiring
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Have partnered with product managers to define roadmaps, scope problems, and drive user-focused outcomes
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Have a deep understanding of modern ML system design, including data processing, training, retrieval, evaluation, deployment, and production monitoring
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Are comfortable leading technical decisions and guiding teams through complex modeling and system design trade-offs
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Are an effective communicator who can translate between engineering, ML, product, and business stakeholders
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Are innovation-minded and able to propose creative, practical solutions to ambiguous problems
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Are a collaborative reviewer and a thoughtful teammate who values clarity, feedback, and shared ownership
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Are highly organized, results-oriented, and capable of ensuring steady execution while supporting individual growth
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Measure your success through your teams success and impact
What You'll Do
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Lead and Grow a High-Performing ML Team : Manage, mentor, and develop a team of ML Engineers and Applied ML Scientists, ensuring they are engaged, supported, and set up for long-term success.
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Drive ML Strategy and Execution : Define technical direction, set priorities, and guide the team in building models, retrieval agents, and ML systems that power Kenshos GenAI platform and AI toolkits such as Link and NERD.
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Deliver Production -Grade ML Systems : Ensure the team follows best practices for building robust, scalable, and maintainable ML solutions, including data pipelines, training workflows, retrieval systems, and model deployment.
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Advance Retrieval-Driven AI Agents : Oversee the development and evaluation of LLM-powered agents and grounded retrieval systems that use trusted S&P datasets to produce accurate, verifiable results.
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Shape Product and ML Roadmaps : Collaborate closely with Product Management and cross-functional leaders to identify opportunities, define problem statements, and align ML initiatives with business objectives.
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Promote Engineering Excellence : Establish strong engineering practices, maintain high code quality, and foster a culture of reliability, observability, and continuous improvement across ML systems.
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Hire and Scale the Team : Partner with Talent Acquisition to attract, interview, and onboard exceptional ML engineering talent as the ML organization grows.
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Stay Hands-On Where It Matters : Contribute technically in design reviews, code reviews, modeling decisions, and architecture discussions, while empowering the team to own implementation and execution.
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Ensure Operational Stability : Oversee monitoring, debugging, and performance evaluation of ML systems in production, ensuring reliability and consistent service quality.
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Foster Collaboration Across Kensho : Work with Backend, Infrastructure, Product, and Data teams to ensure ML systems integrate seamlessly into Kenshos broader platform and applications.
Technologies We Love:
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Traditional ML: SKLearn, XGBoost, LightGBM
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ML/Deep Learning: PyTorch, Transformers, HuggingFace, LangChain
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Deployment tools such as: Docker, Amazon EKS, Jenkins, AWS
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EDA/Visualization : Pandas, Matplotlib, Jupyter, Weights & Biases
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Tools/Toolkits: DVC, MosaicML, NVIDIA NeMo, LabelBox
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Techniques : RAG, Prompt Engineering, Information Retrieval, Data Embedding
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Datastores : Postgres, OpenSearch, SQLite, S3
