AI Engineering Lead

Are you ready to embark on an exciting journey where your talents are valued, and your potential is limitless? At Deerfield, we believe in fostering a culture of excellence, where every team member is empowered to make a difference and contribute to our collective success.

Deerfield is the marketing and communications partner of choice. Purpose-built to scale with our clients, our specialist teams craft stories that matter and bridge meaningful connections to advance healthcare products to the people who need them most. Our full-service capabilities are rooted in storytelling and powered by technology to deliver focused strategy, optimized execution, and tangible outcomes. Further, our flexible working model allows us to scale and grow alongside our clients – whether providing expert consultation, serving as a strategic executional partner, or acting as full agency of record.

We are built to serve and designed to deliver.

The AI Engineering Lead is a hands-on technical contributor who helps advance AI from fragmented experimentation to a repeatable, governed production capability, both in how the agency operates internally and in what it delivers for life-sciences clients.

This role is distinct from a research function, a product-owner role, or a Technology Services position. Ownership of the product roadmap remains with Product Development, the innovation agenda remains with Digital Technology & Innovation, and client engineering delivery remains with Technology Services.
The Senior AI Engineer contributes directly to the agency's AI engineering work: building, evaluating, and hardening systems so they are ready to be adopted and deployed to production with appropriate human-in-the-loop quality controls.

This individual brings strong technical depth in architecture and evaluation of applied AI systems, and communicates clearly with both technical and non-technical stakeholders — equally comfortable walking a project lead through an AI workflow and pairing with an engineer to debug it the same day.

• Partner with Digital Technology & Innovation, Product Development, Technology Services, and IT to move AI ideas from pilot into production without duplicating what those teams already own.
• Bring technical judgment to conversations about what AI work is realistic to ship this quarter versus what should stay a pilot.

• Work hands-on to build and maintain the systems that take AI prototypes into production, and mentor other engineers along the way.
• Help build the path from prototype to production: architecture, evaluation, cost and latency, monitoring, and iteration. Demos are not the finish line.
• Put internal AI capabilities into the way teams actually work. Success is adoption and time-to-quality, not a pile of pilots.
• Contribute to responsible-AI practices that match healthcare requirements: privacy, security, model evaluation, and governance that moves at the speed of the work.
• Evaluate vendors and platforms, including LLM providers and integration patterns, for workflow impact, and flag one-off tools that do not scale.
• Champion automation of MLR and other content and compliance workflows as a concrete delivery differentiator.

• Support pitches with technical input on what we can actually ship for a life-sciences client, not a slide about what is possible.
• Sit with client IT, brand, medical, and agency stakeholders. Be credible without needing an engineer on every call.
• Enable non-engineering users. Most of the agency is not a developer. Tools have to be governed and usable.
• Provide input on AI-related vendor and contract decisions.

• 6+ years in software engineering, AI/ML, or technical delivery, including hands-on experience shipping production AI/ML or applied LLM systems.
• Track record taking applied or generative AI from prototype to production, including evaluation, cost, governance, and adoption. Not a research publication list.
• Hands-on builder: comfortable debugging an AI workflow directly and contributing to technical standards.
• Agency or professional-services experience, or a clear record of client-facing delivery in a high-tempo environment. In-house product-only backgrounds with no client muscle will not land.
• Communication: can explain an AI workflow to a non-technical stakeholder and pair with an engineer on implementation the same day.
• Experience in healthcare,…