Forward Deployed Engineer

Bausch + Lomb•US-NJ-Bridgewater, NJ

17d•Hybrid

About The Position

Bausch + Lomb is seeking a Forward Deployed Engineer (FDE) to turn AI from pilots into scalable production capabilities across the enterprise. This is a hands-on builder role embedded within business divisions and corporate functions. You will work directly with teams to understand their needs, develop production-ready AI solutions within real operating environments, and drive sustained adoption and measurable business value. You will work closely with business leaders, functional subject-matter experts, AI Delivery, Data, and IT to deliver as one team. You will bring strong engineering judgment, translate business challenges into practical solutions, and share reusable approaches that help accelerate AI delivery across Bausch + Lomb.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.
Relevant experience in software engineering, solution implementation, or digital transformation, with a strong record of shipping production-quality solutions.
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, machine learning, and workflow automation in production environments.
Demonstrated experience deploying enterprise technology directly with business users and driving sustained adoption.
Ability to collaborate with and influence cross-functional teams while remaining a hands-on technical contributor.
Strong analytical, troubleshooting, communication, and stakeholder-management skills, with the ability to translate between business needs and technical requirements.
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 delivering technology solutions across multiple business divisions or corporate functions in a complex global organization.
Experience in pharmaceutical, medical device, healthcare, or another regulated industry.
Experience with Microsoft Copilot, Azure AI services, Claude, or other enterprise AI platforms.
Experience with data platforms such as Snowflake, Microsoft Fabric, OneLake, or similar, and with validated-system or controlled enterprise environments.
Experience supporting large-scale digital transformation, change, or enterprise adoption programs.

Responsibilities

Build and deploy AI-enabled solutions, including generative AI, agents, machine learning, and workflow automation, that operate reliably in enterprise environments.
Integrate solutions with enterprise applications, data platforms, and business systems 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, documentation, data privacy, and risk-management practices appropriate to a regulated global enterprise.
Embed and diagnose: Work side by side with business leaders, functional experts, and end users to understand how work is performed and where technology can create value.
Assess workflows, identify high-value use cases, and redesign processes around AI rather than layering tools onto legacy steps.
Resolve technical, process, and adoption issues quickly in partnership with stakeholders.
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.
Scale capabilities across the enterprise: Contribute to technical standards for code quality, deployment, documentation, security, and responsible AI.
Partner with business owners and AI leadership to prioritize high-value opportunities across divisions and functions.
Build reusable assets, including patterns, templates, connectors, and playbooks, so each deployment makes the next one faster.
Share knowledge and mentor colleagues through technical reviews, communities of practice, and hands-on collaboration.
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 solutions.