Lead AI Engineer, Agentic AI Solutions

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, Agentic AI Solutions will design, build, and deploy 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 create intelligent systems that drive measurable business outcomes while meeting enterprise standards for governance, reliability, and compliance. You will help shape the future of AI within a global healthcare organization, building enterprise-scale solutions that directly influence business decisions and customer engagement while working with modern AI technologies, cloud platforms, and large-scale data ecosystems.

Key Responsibilities Design, build, and operate production Applied agents across the commercial process - brand strategy, customer insights, personalized engagement, and closed-loop feedback composing foundational agents rather than rebuilding them.

Build performance investigation agents that autonomously decompose brand performance shifts across geography, channel, payer, and eye care professional (ECP) segment, returning sourced, reproducible narratives to brand and insights teams.

Build pre-call, post-call, and next-best-action / next-best-engagement agents that assemble ECP briefs from Glimpse (Next Best Action) platform / CRM attach explainable reasoning to every recommendation and write structured data back to CRM.Build measurement and feedback agents supporting A/B and multivariate test design, readout, and write-back into the planning loop.

Ground all agents in the enterprise semantic layer and commercial knowledge graph so metric definitions, joins, grain, and row- and role-level access are resolved before query generation rather than corrected afterward.

Implement determinism and reproducibility controls, including validate-then-lock specification patterns, so any number that drives a targeting, content, or investment decision returns the same result across runs and is fully auditable.

Integrate agents with enterprise systems;
Glimpse (NBA platform) /Veeva or Salesforce, Adobe and internal commercial platforms using Model Context Protocol (MCP), agent-to-agent (A2A) patterns, tool discovery, scoped authentication, and structured outputs.

Build and maintain evaluation harnesses per agent, including golden datasets, calibrated LLM-as-judge scoring, and regression testing, and track agent KPIs covering task completion, grounding, reproducibility, human-override rate, latency, and cost per task.

Design for bounded autonomy: agents prepare, propose, investigate, summarize, and route, while humans in loop approve claims, privacy-sensitive actions, and validated-record changes.

Operate agents in production, owning observability and tracing, Evals, token and cost controls including loop caps, model routing and fallback, latency budgets, versioning, rollback, on-call response, and postmortems.

Partner with Medical, Legal, Regulatory, Privacy, Compliance, and IT to bring Responsible AI standards into the design phase rather than treating them as a final gate.

Partner directly with brand teams, field operations, market access, and commercial analytics to translate business asks into agent specifications tied to a defined decision, and to decline use cases where an agent is the wrong tool.

Participating in Agile AI Operations, including backlog management, sprint planning,…