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Director, Applied AI Engineering - Life Sciences Markets

Vi•United States•🌍 Remote
Full-timeSenior
👁️ 0 views•📝 0 applications•Posted 11/6/2026•Expires 1/5/2027
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Job Description

Description Build and lead the technical delivery of AI applications within Vi ’s Life Science Markets squad. The squad turns large, varied proprietary datasets into products that help evaluate pharmaceutical markets, treatment adoption, competitive dynamics, and drug commercial performance.

This is primarily a hands-on role. You will spend most of your time designing systems, writing production code, integrating models, and shipping AI workflows, while owning technical standards and overall squad delivery. A background in life sciences or capital markets is very helpful, but not required.

What You’ll Own Hands-on delivery: Personally build and ship core AI services and workflows. Own critical implementation work and resolve technical blockers alongside engineers, data scientists, and domain experts. Squad leadership & management: Lead the engineers in the squad and the overall execution.

Set implementation standards through your own work, review code and architecture, and coordinate technical priorities and dependencies. Strategy and roadmap: Identify the life sciences market questions where Vi ’s data can add value. Prioritize use cases, define success measures, and turn them into a focused roadmap.

AI architecture and implementation: Design and implement systems combining ML models, LLMs, retrieval, and workflow automation to address life sciences market questions. Make practical choices about model quality, reliability, latency, and cost.

Engineering and evaluation quality: Build automated tests, evaluation datasets, and monitoring for AI outputs and application reliability. Maintain rigorous validation of predictive models, including point-in-time backtesting, baseline comparisons, and checks for leakage and coverage bias.

Production ownership: Take prototypes into production and own models, APIs, integrations, deployment, observability, and incident resolution. Build reusable capabilities that scale across therapies, companies, and clients. Client partnership

Required Skills

LeadershipMachine Learning

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