Senior AI Data Engineer

Senior AI Data Engineer

Cooley AI is seeking a Senior AI Data Engineer to join the Innovation team.

About Cooley AI: Cooley AI is the wholly-owned subsidiary of Cooley LLP. The frontier development arm of the leading technology law firm in the world. Our mission is to bring the best-in-class practices from the technology field into the legal domain to help lead our firm and the legal industry into the AI era. All employees of Cooley AI are employees of Cooley and are seconded to Cooley AI.

Position summary: As a leading technology law firm, Cooley is determined to become a leader in the digital practice of law. The Senior AI Data Engineer is a senior technical individual contributor within the Data Products function, responsible for building and maintaining the AI infrastructure layer that powers the firm’s AI-powered data applications on the Northstar Lakehouse. Working as a peer to the Senior Data Application Engineer, this role is responsible for the intelligence layer beneath the application: the RAG pipelines, embedding infrastructure, vector search implementation, LLM integration patterns, and AI feature observability that make AI-powered legal intelligence products reliable, performant, and trustworthy in production. The Senior AI Data Engineer brings production AI engineering depth to a greenfield AI application build, operating at the intersection of data engineering, applied machine learning, and enterprise application development. This is a high-impact individual contributor role and will build production RAG and LLM-powered systems with the understanding of what it takes to make AI features work reliably at scale. Specific duties and responsibilities include, but are not limited to, the following:

Position responsibilities:

RAG Pipeline & Retrieval Infrastructure:

• Design, build, and maintain production RAG pipelines that ingest, process, chunk, embed, and index firm data assets for retrieval by AI-powered legal intelligence applications. Own the full RAG pipeline lifecycle from document ingestion through retrieval quality monitoring
• Own the vector search implementation including index design, embedding model selection and maintenance, retrieval strategy configuration, and ongoing retrieval quality assessment. Ensure the vector search layer returns accurate, contextually relevant results
• Build and maintain embedding pipelines that transform structured and unstructured firm data assets into vector representations consumable by retrieval systems and LLM-powered features. Apply chunking strategies, metadata enrichment, and embedding quality validation appropriate to legal sector document complexity
• Monitor and maintain retrieval quality in production, establishing metrics for retrieval relevance, embedding freshness, index coverage, and pipeline execution health that surface degradation before it affects application users
• Collaborate with the Data Engineering & Platform function to ensure RAG pipeline data sources are correctly integrated with the Northstar Lakehouse Silver and Gold layer data assets, consuming governed, certified data rather than bypassing platform governance controls

LLM Integration & AI Feature Engineering:

• Build and maintain the LLM integration layer for Data Applications squad products, implementing prompt engineering patterns, output validation frameworks, fallback handling, latency management, and cost-aware API usage patterns that make LLM-powered features reliable in production
• Implement AI output handling patterns that ensure application features degrade gracefully when model responses are unexpected, latency is high, or retrieval quality issues affect the context provided to the LLM. Design for failure from the outset rather than retrofitting resilience
• Build and maintain AI feature observability tooling that provides the Data Applications squad with visibility into LLM response quality, retrieval relevance, token consumption trends, latency patterns, and user interaction signals that inform ongoing AI feature improvement
• Evaluate and integrate Databricks-native AI capabilities including FMAPI, Mosaic AI, Databricks Model Serving, and Databricks Vector Search as they mature, making architectural recommendations on adoption timing and integration patterns alongside the Platform Architect
• Apply responsible AI practices to all AI feature implementations including LLM output validation, bias awareness in retrieval systems, appropriate …