Lead AI Engineer, Agentic AI Solutions

Bausch+Lomb Companies Inc.•Bridgewater, NJ

1d•$150,000 - $200,000•Hybrid

About The Position

The Lead AI Engineer, Agentic AI Solutions designs, builds, and deploys AI-powered solutions that support commercial decision-making across brand strategy, customer engagement, market access, and analytics. This highly technical individual-contributor role combines AI engineering, software development, and business collaboration to deliver measurable outcomes while meeting enterprise standards for governance, reliability, and compliance. The role helps shape AI capabilities within a global healthcare organization, using modern AI technologies, cloud platforms, and large-scale data ecosystems. The engineer builds and operates production agents, grounds them in enterprise semantic and commercial knowledge layers, integrates them with business systems, and establishes controls for determinism, reproducibility, evaluation, observability, and bounded autonomy. The position partners with business, technical, and oversight teams to translate needs into agent specifications, operate solutions in production, and contribute reusable patterns and mentorship.

Requirements

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related field.
At least 5 years of experience building and deploying production software, AI, or ML solutions, including LLM-powered applications.
Strong Python development skills and software engineering practices, including testing, CI/CD, APIs, code reviews, and containerization.
Hands-on experience with AI agent frameworks such as LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, or similar.
Experience with tool calling, structured outputs, function schemas, and enterprise system integrations.
Experience designing and implementing RAG solutions, vector databases, embeddings, and retrieval systems.
Strong SQL and data modeling skills, including ability to define and validate business metrics.
Experience developing frameworks and metrics to evaluate AI/LLM performance.
Experience with cloud and MLOps platforms such as AWS, Azure, Databricks, and Snowflake, as well as orchestration tools.
Experience operating in regulated environments with a focus on governance, compliance, and auditability.
Ability to collaborate effectively across technical and business functions.
Experience working in Agile environments using Scrum, SAFe, or similar methodologies.
Strong communication and presentation skills, including ability to explain complex technical concepts to non-technical audiences.
Authorization to work for any employer in the United States; visa sponsorship is not available.

Nice To Haves

Experience with pharmaceutical, medical device, or consumer health commercial data, including prescription, claims, payer, CRM, and sales data.
Familiarity with eye care industry dynamics, including ophthalmology, optometry, and Eye Care Professional channels.
Experience with Veeva and/or Salesforce integrations.
Experience with knowledge graphs, semantic modeling, and semantic layer tools.
Familiarity with healthcare compliance requirements, including HIPAA, GDPR, FDA promotional regulations, and MLR/PRC review processes.
Experience with LLM observability, monitoring, and AI cost management.
Experience working in regulated industries with strong governance, auditability, and compliance requirements.

Responsibilities

Design, build, and operate production agents for commercial processes including brand strategy, customer insights, personalized engagement, and feedback.
Build performance investigation agents that analyze brand performance changes across geography, channel, payer, and ECP segments and produce sourced, reproducible narratives.
Build pre-call, post-call, and next-best-action or next-best-engagement agents that assemble ECP briefs, connect with the Glimpse platform and CRM, explain recommendations, and write structured data back to CRM.
Build measurement and feedback agents for A/B and multivariate test design, readouts, and write-back into planning processes.
Ground agents in the enterprise semantic layer and commercial knowledge graph, resolving metric definitions, joins, data grain, and row- and role-level access before query generation.
Implement determinism and reproducibility controls, including validate-then-lock specifications, to ensure decision-driving results are consistent and auditable.
Integrate agents with enterprise systems such as Glimpse, Veeva or Salesforce, Adobe, and internal commercial platforms using MCP, A2A patterns, tool discovery, scoped authentication, and structured outputs.
Build and maintain agent evaluation harnesses, including golden datasets, calibrated LLM-as-judge scoring, regression testing, and KPIs for task completion, grounding, reproducibility, human overrides, latency, and cost.
Design agents for bounded autonomy, with human approval for claims, privacy-sensitive actions, and validated-record changes.
Operate agents in production, managing observability and tracing, evaluations, token and cost controls, model routing and fallback, latency budgets, versioning, rollback, on-call response, and postmortems.
Partner with Medical, Legal, Regulatory, Privacy, Compliance, and IT to incorporate Responsible AI standards from the design phase.
Work with brand teams, field operations, market access, and commercial analytics to translate business needs into agent specifications tied to defined decisions, and assess when an agent is not an appropriate solution.
Participate in Agile AI Operations, including backlog management, sprint planning, cross-functional collaboration, and continuous improvement.
Contribute reusable patterns, components, and documentation to the shared agent pattern library, and mentor engineers and analysts.