AI & Data Engineer, Data Discovery Services

Job description

Working with Us Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible. Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us. Position Summary: Join the Data Discovery Services team within Enterprise Data Platforms, where we deliver and maintain the data foundation platforms that power discovery, search, and data accessibility across the Bristol Myers Squibb enterprise. We run multiple search and discovery services used across the company -- and we're building the next generation of AI-powered discovery on top of them. Our work makes enterprise data findable, accessible, and actionable for teams across the organization. At the core of this is a semantic knowledge layer -- metadata, taxonomies, and relationships that describe what data means and how it connects -- curated as a data inventory that helps AI work reliably across the enterprise. This is a high-impact team where engineering, search, and applied AI come together to solve real problems at scale. As an AI / Data Engineer, you'll be a hands-on Python developer building the pipelines and integrations that make enterprise data more discoverable. Your primary focus is data engineering -- pipelines, metadata enrichment, transformations, and platform integrations. You'll also contribute to search and AI-powered retrieval as you grow into the role. Working alongside data engineers, search engineers, and data scientists, this is a hands-on engineering role -- you'll write code, build pipelines, ship features, and own what you deliver. Why Join Us? Work with a modern stack -- Databricks, Amazon Web Services (AWS), OpenSearch, vector search, semantic knowledge layers, graph databases, and AI agents. Build real AI-powered discovery capabilities, not proofs of concept. Grow your skills across data engineering, search, and applied AI on the same team. Use AI-assisted development tools (Claude, Copilot) in your daily workflow. Contribute to open-source projects and shared accelerators. Clear path to grow into senior engineering, search specialization, or AI engineering roles. Make enterprise data findable and accessible for teams working to improve patient outcomes. Job Responsibilities: As an AI / Data Engineer, you'll be a hands-on Python developer building the pipelines and integrations that make enterprise data more discoverable. Your primary focus is data engineering — pipelines, metadata enrichment, transformations, and platform integrations. You'll also contribute to search and AI-powered retrieval as you grow into the role. Working alongside data engineers, search engineers, and data scientists, this is hands-on engineering role — you'll write code, build pipelines, ship features, and own what you deliver. Build and maintain Python pipelines that pull metadata from enterprise data catalogs, enrich it with taxonomy tags and ownership information, and publish it to the discovery platform. Tune and optimize search indexes -- adjust analyzers, boost fields, and test queries -- to ensure results match what users need. Build a semantic knowledge layer -- chunking documents, generating vector embeddings, and enriching them with semantic knowledge metadata -- to grow a data inventory that supports retrieval-augmented generation (RAG) and helps AI systems and large language models (LLMs) find and use the right context. Maintain integrations that sync ontology and taxonomy changes into the discovery platform, so classifications stay current. Investigate and resolve data pipeline issues across Databricks and AWS Glue, trace root causes through metadata enrichment flows, and add data quality checks to prevent recurrence. Build API endpoints and Model Context Protocol (MCP) servers that expose search and metadata capabilities to applications and AI agents. Design metadata pipelines that map cross-domain dataset relationships and add them to the cross-domain join catalog with confidence scores. Analyze search patterns, capture user feedback, and improve the discovery experience so the system learns and improves over time. Qualifications & Experience: Required Bachelor’s degree in computer science, Data Science, Information Science, Engineering, or a related field. Master's degree preferred. Demonstrated proficiency in data engineering, software engineering, or a related technical discipline, with a track record of delivering production data pipelines. Proficient Python skills -- this is your primary language day-to-day. Proficiency in Structured Query Language (SQL). Experience with Databricks and AWS Glue for data pipelines and transformations. Solid data engineering fundamentals: extract-transform-load (ETL/ELT) patterns, data modeling, data quality, and pipeline orchestration. Familiarity with AWS cloud services (S3, Lambda, API Gateway, Glue). Experience with OpenSearch or Elasticsearch. Understanding of metadata management and data cataloging concepts. Effective problem-solving skills and willingness to learn. Good communication skills and ability to work collaboratively in a team. Preferred Qualifications: Experience with semantic knowledge layers, RAG patterns, vector search technologies, and building AI-ready data inventories. Familiarity with semantic search, embeddings, chunking strategies, relevance tuning, and semantic knowledge metadata (entity relationships, taxonomies, context enrichment). Exposure to AI agent patterns, MCP, or large language model orchestration frameworks. Experience with ontology or taxonomy technologies (such as Turtle, Resource Description Framework, Web Ontology Language, or SPARQL query language) or management platforms. Familiarity with graph databases or knowledge graph technologies. Experience with metadata enrichment, data lineage, or data quality frameworks. Exposure to Azure OpenAI, Google Vertex AI, or Amazon Bedrock. Experience with Docker, Elastic Container Service (ECS), or CloudFormation. Prior exposure to pharma or life sciences. If you come across a role that intrigues you but doesn’t perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career. Compensation Overview: Princeton - NJ - US: $87,810 - $106,399 The starting compensation range(s) for this role are listed above for a full-time employee (FTE) basis. Additional incentive cash and stock opportunities (based on eligibility) may be available. The starting pay rate takes into account characteristics of the job, such as required skills, where the job is performed, the employee’s work schedule, job-related knowledge, and experience. Final, individual compensation will be decided based on demonstrated experience. Eligibility for specific benefits listed on our careers site may vary based on the job and location. For more on benefits, please visit https://careers.bms.com/life-at-bms/. Benefit offerings are subject to the terms and conditions of the applicable plans in effect at the time and may require enrollment. Our benefits include: Health Coverage: Medical, pharmacy, dental, and vision care. Wellbeing Support: Programs such as BMS Well-Being Account, BMS Living Life Better, and Employee As