Manager, AI Engineering

Position Summary

As a Manager, AI Engineering at Trinity Life Sciences, you are a hands‑on builder who leads by doing.

You’ll embed with biopharma and commercial Life Sciences teams, absorb their hardest problems, and rapidly design and build working GenAI solutions e.g., client‑facing Copilots, RAG pipelines over clinical and commercial data, and agentic workflows that change how teams operate.

You’ll move from ambiguous business challenge to running prototype in days, not quarters; demo it, iterate live with clients, and make it better. You will write code, own deployments, and be accountable for real outcomes.

Within a week, you might be turning a messy CRM export into a live sales rep facing AI assistant that surfaces next‑best‑action recommendations.

Or you might be building a RAG pipeline over 10 years of clinical trial data so a market access team can answer payer questions in seconds. As a Manager, you’ll also guide a small team of AI engineers (1–3), helping them ramp faster, navigate client complexity, and grow into the role.

You lead by pairing, reviewing, unblocking, and raising the bar on engineering quality and client impact.

What You’ll Do

Technical delivery:


* Embed with clients at top biopharma and life sciences organizations: Sit with their commercial, medical affairs, and data teams to surface unmet needs and translate them into engineered solutions.


* Prototype at speed: Stand up GenAI‑powered applications, Copilots, RAG pipelines, agentic workflows, and data integrations fast enough to make a client’s jaw drop within two weeks of receiving their data.


* Own delivery end‑to‑end: From the first whiteboard session through production‑ready code, demo, feedback loop, and handoff to product.


* Flex between missions: Move between active client engagements and product sprints as priorities shift.


* Make the model and our products better: Document patterns, share playbooks with the cohort, and push field learnings back into the product, including reusable components and Copilot templates.

Team development:


* Provide technical leadership and guidance to junior AI engineers: Pairing on hard problems, reviewing their work, and giving them direct, useful feedback.


* Help new engineers ramp up through the onboarding path: Accelerate their time to product fluency and first live pod contribution.


* Serve as a technical sounding board when engineers are stuck: Ask questions that guide them to the solution and help them grow.


* Model the culture: Show what great client engagement, clean iteration, and knowledge‑sharing look like in practice.

What You'll Bring


* 6–9 years of professional software engineering experience, with a track record of shipping production‑quality systems.


* Preferred experience mentoring or leading small engineering teams, formally or informally.


* Strong proficiency in Python; fluency with cloud services (Azure, AWS, ...