Machine Learning Engineer II

About the role

Kensho is S&P Global’s hub for AI innovation and transformation, with expertise in machine learning, natural language processing, and data discovery. The DRIVE Team focuses on designing and deploying production-grade machine learning systems that power next‑generation agentic search pipelines, building robust retrieval systems, scalable embedding infrastructure, and tightly integrated LLM pipelines that leverage unstructured data. We are seeking a mid‑level Machine Learning Engineer to help develop and scale RAG systems across the company in a hands‑on, full‑lifecycle ML role emphasizing retrieval models, LLM orchestration, and system‑level thinking.

Responsibilities

• Design and implement end-to-end RAG pipelines that integrate proprietary chunking algorithms, embedding models, vector databases, and data retrieval agents
• Build and optimize retrieval systems over large-scale proprietary datasets using advanced embedding techniques
• Develop LLM-based solutions that orchestrate retrieval, generation, and ranking to deliver high-quality, context-aware responses
• Investigate and solve challenges in vector search, chunking and indexing strategies, unstructured data retrieval evaluation, and GraphRAG
• Work closely with Product and Design teams to build ML-based solutions that enhance user experiences and meet business objectives
• Collaborate closely with the ML Operations team to create automated solutions for managing the entire ML systems lifecycle, from initial technical design to seamless implementation

Requirements

• Bachelor's degree or higher in Computer Science, Engineering, or a related field
• 3+ years of significant, hands-on industry experience with machine learning, natural language processing (NLP), information retrieval systems and large-scale text processing, including designing, shipping, and maintaining production systems
• Strong programming skills in Python, with a working knowledge of data processing tools and ML frameworks such as PyTorch, Transformers, and HuggingFace
• Experience working with machine learning libraries/frameworks for Large Language Model (LLM) orchestration, such as Langchain, LLamaIndex, etc.
• Proven experience building ML pipelines for data processing, training, inference, maintenance, evaluation, versioning, and experimentation
• Experience working with vector databases (e.g., PostgreSQL/PGVector, OpenSearch, Pinecone) and understanding of similarity search techniques and vector indexing algorithms
• Demonstrated effective coding, documentation, collaboration, and communication habits
• Strong problem-solving skills and a proactive approach to addressing challenges
• Ability to adapt to a fast-paced