Bausch + Lomb is hiring a Lead AI Engineer to help build governed foundational AI systems for knowledge and semantic retrieval. In this individual contributor role, you will design the semantic layer, knowledge graph structures, and end-to-end retrieval stack that enterprise AI solutions rely on for accurate, scalable, and compliant insights.
Based in Bridgewater, NJ (onsite), this position supports a hybrid work schedule with up to 2 days/week telecommuting and 3 days/week in the corporate office.
Role summary
You will lead the development and maintenance of the semantic foundation that enables agents and BI to consume certified metric and entity definitions. The work spans semantic modeling, ontology and knowledge graphs, temporal validity, entity resolution, and retrieval quality measurement, with interfaces designed for agent tool use.
Responsibilities
- Design, build, and maintain a governed semantic layer for commercial definitions, including canonical metric definitions, dimensions, hierarchies, joins, grain, and row- and role-level access policies.
- Codify commercial metric definitions such as TRx, NRx, NBRx, NC TRx, market share, market basket, decile and segment assignment, and projected versus captured volume, including source system, calculation, refresh cadence, and known limitations; arbitrate conflicting definitions across brand, field, market access, and finance.
- Build constraints to prevent semantically meaningless queries, including combinations that are technically joinable but yield misleading results (for example, mixing grains, blending projected and actual volumes, or computing share across non-comparable sources).
- Design and extend a 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.
- Apply entity resolution to reconcile prescriber and account identity across prescription, claims, specialty pharmacy, and CRM feeds, maintaining group practice and affiliation structures, including overlapping roles (prescriber, account contact, and point of dispense).
- Maintain taxonomies and controlled vocabularies, including synonym and alias mappings to ensure consistent disambiguation across users and brands.
- Encode temporal validity for territory realignments, formulary changes, affiliation changes, and segment refreshes so historical questions return historically correct answers.
- Own the retrieval stack end to end, including chunking strategy, embedding model selection and evaluation, and index configuration and tuning.
- Measure retrieval quality using labeled query sets and track recall and precision at k, groundedness, and citation accuracy, with regression testing to prevent silent downstream degradation.
- Manage reindexing, embedding drift, model migration, and vector infrastructure cost and performance at production scale.
- Expose semantic and retrieval capabilities to agents through well-specified interfaces, including Model Context Protocol (MCP) servers, resources, tool contracts, and structured schemas.
- Partner with Applied AI Agents engineers as the primary upstream dependency on a shared sprint cadence, with evaluation tied in part to agent answer accuracy and delivery velocity.
- Diagnose grounding failures by determining whether issues originate from definitions, entity resolution, retrieval, or model reasoning, then remediate what belongs in the layer.
- Operate a federated contribution model by reviewing proposed metrics, entities, and relationships from analysts and agent engineers to extend the canonical model without fragmentation.
- Collaborate with Data Engineering, IT, Privacy, and Compliance to enforce access policy and minimum-necessary handling, including PDRP and HIPAA audit trails at the definition and retrieval layer.
- Document and socialize the model and run enablement so analysts and engineers adopt governed definitions instead of rebuilding locally.
Requirements
- 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 such as dbt Semantic Layer, MetricFlow, Cube, LookML, or 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 across business and technical teams.
- Experience working in Agile environments using Scrum, SAFe, or similar methodologies.
- Excellent communication and documentation skills to align stakeholders on data definitions and standards.
Technologies
- Python, SQL, Azure, AWS, Databricks, Snowflake
- Knowledge Graphs, Semantic Modeling, Vector Databases, Retrieval Systems
- dbt Semantic Layer, MetricFlow, Cube, LookML, AtScale
- Neo4j
- LLM-enabled solutions: RAG, tool calling, structured outputs, MCP
- CI/CD, testing, version control, containerization, dbt, Airflow, Dagster
- Scrum, SAFe, PDRP, HIPAA
Benefits
- Medical, dental, vision insurance
- Disability and life insurance
- 401(k) plan and company match
- Tuition reimbursement program (select degrees)
- Company holidays, well-being benefits, sick time
- Floating holidays, paid vacation
- Short-term and/or long-term incentives
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 industries with strong audit and compliance standards.
Compensation: For U.S. locations that require disclosure of compensation, the starting pay range is $150,000.00 to $200,000.00 for this role.
Work authorization: Applicants must be authorized to work for any employer in the U.S. Bausch + Lomb is unable to sponsor or take over sponsorship of an employment Visa at this time.
