Manager, AI Engineering

Trinity Life Sciences•Princeton, NJ

6d•$148,000 - $222,000•Hybrid

About The Position

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.

Requirements

6–9 years of professional software engineering experience, with a track record of shipping production‑quality systems.
Strong proficiency in Python; fluency with cloud services (Azure, AWS, or GCP).
Hands‑on experience building or integrating GenAI systems: LLMs, prompt engineering, RAG, agentic architectures, multi‑modal pipelines, and Copilot‑style assistants.
Real comfort operating in client‑facing environments: You have presented to non‑technical stakeholders and navigated their feedback without losing your footing.
Thrive on pace: Compressed timelines energize rather than exhaust you; you do your best work when the deadline is real.
Understand before you build: You invest the time to genuinely absorb what a client needs before you write a line of code.
Are collaborative by default: You pull in teammates, ask for help early, and share credit.
Learn fast in unfamiliar domains: You have cracked new industries, new stacks, or new problem spaces quickly before, and you can do it again.
Make others better: You have the patience, directness, and generosity to mentor and coach engineers; you understand that growing someone else is a core contribution of the value you bring to our firm.
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent experience).

Nice To Haves

Preferred experience mentoring or leading small engineering teams, formally or informally.
Experience in Life Sciences or Biotech preferred: You understand the commercial model, can talk the language, and understand the goals of stakeholders in the industry.

Responsibilities

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.
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.