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Head of Forward Deployed Engineering

Job in Santa Fe, Santa Fe County, New Mexico, 87503, USA
Listing for: Bausch + Lomb
Full Time position
Listed on 2026-10-13
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Change Management
Salary/Wage Range or Industry Benchmark: 190000 - 240000 USD Yearly USD 190000.00 240000.00 YEAR
Job Description & How to Apply Below

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 worldfrom 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.

Position overview

Bausch + Lomb is seeking a Head of Forward Deployed Engineer (FDE) to turn AI from pilots into production capability across B+L. This is a hands‑on builder role embedded in manufacturing, supply chain, quality, procurement, engineering, and logistics teams. You will ship production‑ready AI solutions inside real operating environments and make sure people use them.

You will also lead a small team of FDEs as a player‑coach: you build alongside them, set the technical standard, and develop their skills. You will work closely with business leaders, AI Delivery, Data, and IT to deliver as one team.

How the role works

  • Roughly 60-70% hands‑on engineering: building, integrating, testing, and hardening solutions to production standards.

  • The remainder is split between process discovery, change management and adoption, and coaching the FDE team.

  • Success is measured by adoption and business outcomes, not by prototypes delivered.

Key responsibilities

Build and deploy

  • Design, code, and deploy AI‑enabled solutions (generative AI, agents, ML, workflow automation) that run reliably.

  • Integrate with enterprise systems such as SAP and plant, quality, and supply chain platforms through APIs and data pipelines.

  • Own the path from prototype to production: testing, user acceptance, security and compliance review, release, monitoring, and post‑deployment support.

  • Apply validation and documentation practices appropriate to a regulated environment.

Embed and diagnose

  • Work side by side with plant leaders, planners, buyers, and quality teams to understand how work actually gets done.

  • Run workflow assessments, identify high‑value use cases, and redesign processes around AI rather than layering tools on top of legacy steps.

  • Resolve technical and operational issues quickly in the field.

Drive adoption and change

  • Treat adoption as a deliverable. Define adoption and value metrics for every deployment and track them after go‑live.

  • Build training materials, user guides, and deployment playbooks. Facilitate workshops and train end users.

  • Identify and develop local champions within each function.

  • Capture user feedback and deployment learnings and feed them to the Enterprise AI team to shape product priorities.

Lead the FDE team

  • Coach and mentor FDEs, setting standards for code quality, deployment, and documentation.

  • Prioritize work across GPS functions with business owners and the AI leadership team.

  • Build reusable assets (patterns, templates, connectors, playbooks) so each deployment makes the next one faster.

Collaborate across the enterprise

  • Partner with AI Delivery, Data, and IT on architecture, data access, security, and platform standards.

  • Escalate platform gaps and reuse enterprise components instead of building one‑off…

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