Sr. Engineer, Forward Deployed GenAI
About The Position
As a Forward-Deployed Engineer at Trinity Life Sciences, you are embedded directly inside biopharma and commercial Life Sciences teams: absorbing their hardest problems, then building working solutions fast. You'll move from ambiguous business challenge to running prototype in days, not quarters. You'll demo it, iterate live with clients, and make it better. Repeat. You will write real code, own real deployments, and be accountable for real outcomes. For example, 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 instead of days.
Requirements
- 5–8 years of professional software engineering experience, with a track record of shipping production-quality systems
- Strong proficiency in Python and/or Node.js/React.js; fluency with cloud services (Azure, AWS, or GCP)
- Hands-on experience building or integrating GenAI systems; LLMs, prompt engineering, RAG, agentic architectures, or multi-modal pipelines
- Real comfort operating in client-facing environments: you have presented to non-technical stakeholders and navigated their feedback without losing your footing
- Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent experience)
Nice To Haves
- Experience in Life Sciences or Biotech strongly preferred: you understand the commercial model, can talk the language, and understand 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, 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 sprint 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
Benefits
- annual discretionary performance bonus
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What This Job Offers
Job Type
Full-time
Career Level
Senior
