Head of Forward Deployed Engineering

Bausch+Lomb Companies Inc.•USA - Remote, OR

3d•$190,000 - $240,000•Onsite

About The Position

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. 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.

Requirements

Bachelor's degree in computer science, Engineering, Information Systems, or a related field.
10+ years in software engineering, solution implementation, digital transformation, with a strong record of shipping production code.
Proficiency in Python and modern API and integration development, with experience building on cloud platforms such as Microsoft Azure or Google Cloud.
Hands-on experience with generative AI, agents, ML, and workflow automation in production.
Demonstrated experience deploying enterprise technology directly with business users, including driving adoption.
Experience leading or mentoring engineers while remaining an active contributor.
Strong analytical, troubleshooting, and stakeholder management skills, with the ability to translate between plant floor and engineering language.
Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

Nice To Haves

Experience in manufacturing, supply chain, pharmaceutical, medical device, healthcare, or other regulated industries.
Experience with Microsoft Copilot, Azure AI services, Claude, or other enterprise AI platforms.
Experience with data platforms (Snowflake, Microsoft Fabric, OneLake, or similar) and with GxP or validated-system environments.
Experience leading large-scale digital transformation or change programs.

Responsibilities

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.
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.
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.
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.
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 solutions.