US NJ Bridgewater Lead AI Engineer, Knowledge & Semantic Systems CA

Job description

Bausch + Lomb (NYSE/TSX: BLCO) is a leading global eye health company dedicated to protecting and enhancing the gift of sight for millions of people around the world—from the moment of birth through every phase of life. Our mission is simple, yet powerful: helping you see better, to live better. Our comprehensive portfolio of over 400 products is fully integrated and built to serve our customers across the full spectrum of their eye health needs throughout their lives. Our iconic brand is built on the deep trust and loyalty of our customers established over our 170-year history. We have a significant global research, development, manufacturing and commercial footprint of approximately 13,000 employees and a presence in approximately 100 countries, extending our reach to billions of potential customers across the globe. We have long been associated with many of the most significant advances in eye health, and we believe we are well positioned to continue leading the advancement of eye health in the future. Overview The Lead AI Engineer, Knowledge & Semantic Systems will build and maintain the foundational data, knowledge, and retrieval systems that power enterprise AI solutions. This highly technical individual contributor role focuses on creating trusted semantic models, knowledge graphs, metadata frameworks, and retrieval capabilities that enable accurate, scalable, and compliant AI-driven insights across the organization. You will help shape the data and knowledge foundation supporting enterprise AI capabilities while collaborating across data engineering, AI, analytics, and business teams to deliver innovative solutions that improve decision-making and business performance. Key Responsibilities Design, build, and maintain the governed semantic layer for commercial; canonical metric definitions, dimensions, hierarchies, joins, grain, and row- and role-level access policy so that agents and BI consume one certified definition rather than reconstructing business logic per query. Codify commercial metric definitions including TRx, NRx, NBRx, NC TRx, market share and market basket, decile and segment assignment, and projected versus captured volume, capturing source system, calculation, refresh cadence, and known limitations for each, and arbitrate conflicting definitions across brand, field, market access, and finance. Build constraints that prevent semantically meaningless queries; combinations that are technically joinable but produce misleading results, such as blending projected and actual volumes, mixing incompatible grains, or computing share across non-comparable sources and enforce them in the layer rather than documenting them and hoping. Design and extend the commercial ontology and knowledge graph, covering ECP, account, affiliation and parent-child hierarchy, payer and plan, formulary status, product, territory and alignment, content asset, and engagement, modeling the relationships agents traverse rather than leaving them to be inferred. Work with enterprise team and apply entity resolution to Semantic definitions across sources used for Agents, reconciling prescriber and account identity across prescription, claims, specialty pharmacy, and CRM feeds, maintaining group practice and affiliation structures, and handling the reality that an eye care professional may be prescriber, account contact, and point of dispense simultaneously. Maintain taxonomies, controlled vocabularies and synonym and alias mappings so that varied business phrasings resolve onto the same governed concept, and so agents disambiguate consistently across users and brands. Encode temporal validity for territory realignments, formulary changes, affiliation changes, and segment refreshes, so historical questions return historically correct answers rather than today’s structure applied to last year’s data. Own the retrieval stack end to end; chunking strategy, embedding model selection and evaluation, index configuration and tuning. Treat retrieval quality as a measured discipline, building labeled query sets and tracking recall and precision at k, groundedness, and citation accuracy, with regression testing so that an embedding, chunking, or corpus change cannot silently degrade every downstream agent. Manage reindexing, embedding drift, model migration, and the cost and performance of vector infrastructure at production scale. Expose semantic, graph, and retrieval capability to agents through well-specified interfaces; Model Context Protocol (MCP) servers and resources, tool contracts, and structured schemas including the disambiguation hints, synonym maps, and worked examples that make correct agent behavior the default. Partner with Applied AI Agents engineers as their primary upstream dependency, operating on a shared sprint cadence and being measured in part on the answer accuracy and delivery velocity of the agents that consume your work. Diagnose grounding failures: when an agent returns a wrong or unsupported answer, determine whether the cause is definition, entity resolution, retrieval, or model reasoning, and remediate what belongs to this layer. Operate a federated contribution model, accepting proposed metrics, entities, and relationships from analysts and agent engineers, reviewing them for consistency, and curating the canonical model so it extends without fragmenting. Partner with Data Engineering, IT, Privacy, and Compliance to enforce access policy, PDRP and HIPAA minimum-necessary handling, and audit trails at the definition and retrieval layer rather than at the application layer. Document and socialize the model and run enablement so analysts and engineers adopt governed definitions instead of rebuilding them locally. Technology Environment Python & SQL • Azure/AWS • Databricks & Snowflake • Knowledge Graphs & Semantic Modeling • Vector Databases & Retrieval Systems Qualifications Required: Bachelor's or Master's degree in Computer Science, Data Engineering, Information Science, Machine Learning, or a related field. 5+ years of experience in data engineering, analytics, semantic modeling, or knowledge engineering. Expertise in SQL, dimensional data modeling, and business metric governance. Hands-on experience with semantic layer tools (e.g., dbt Semantic Layer, MetricFlow, Cube, LookML, AtScale). Experience designing and managing knowledge graphs, ontologies, and graph databases (e.g., Neo4j). Experience with entity resolution, identity management, and master data concepts. Experience with vector databases, retrieval systems, and search optimization. Understanding of LLM-enabled solutions, including RAG, tool calling, structured outputs, and MCP. Strong Python development skills and software engineering best practices (CI/CD, testing, version control, containerization). Experience with cloud data platforms and tools such as Snowflake, Databricks, AWS/Azure, dbt, Airflow, or Dagster. Knowledge of data governance, security, lineage, and compliance requirements. Proven ability to collaborate effectively across business and technical teams. Experience working in Agile environments using Scrum, SAFe, or similar methodologies. Excellent communication and documentation skills, with the ability to align stakeholders on data definitions and standards. Preferred: Experience with pharmaceutical commercial data, including prescription, claims, payer, specialty pharmacy, hub, and CRM data. Familiarity with eye care industry dynamics, including ophthalmology, optometry, and Eye Care Professional (ECP) networks. Experience with customer master data management (MDM) and HCP/HCO data in life sciences. Experience with graph databases (e.g., Neo4j) and vector database technologies. Familiarity with content metadata, classification, and approval workflows. Experience with data governance, cataloging, lineage, and data observability tools. Knowledge of healthcare compliance requirements, including HIPAA and FDA promotional regulations. Experience working in regulated i