Director, Applied AI Engineering – Life Sciences Markets
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Job Description
Director building and leading production AI systems for Vi’s pharmaceutical market analytics platform. Owning ML/LLM architecture, delivery, evaluation, deployment, and client-facing product strategy.
Responsibilities:
- Build and lead technical delivery of AI applications within Vi’s Life Science Markets squad
- Design systems, write production code, integrate models, and ship AI workflows
- Personally build and ship core AI services and workflows
- Own critical implementation work and resolve technical blockers with engineers, data scientists, and domain experts
- Lead squad engineers and overall execution
- Set implementation standards, review code and architecture, and coordinate technical priorities and dependencies
- Identify life sciences market questions where Vi’s data can add value
- Prioritize use cases, define success measures, and create a focused roadmap
- Design and implement systems combining ML models, LLMs, retrieval, and workflow automation
- Make practical choices about model quality, reliability, latency, and cost
- Build automated tests, evaluation datasets, and monitoring for AI outputs and application reliability
- Maintain rigorous predictive-model validation, including point-in-time backtesting, baseline comparisons, and leakage and coverage-bias checks
- Take prototypes into production and own models, APIs, integrations, deployment, observability, and incident resolution
- Build reusable capabilities that scale across therapies, companies, and clients
- Work directly with clients and domain experts to understand analytical workflows, explain findings, challenge assumptions, and turn feedback into product priorities
Requirements:
- Strong record of personally building and shipping production AI applications, with technical ownership of complex systems and measurable business outcomes
- Experience leading technical delivery across functions while remaining a primary contributor to design, modeling, coding, debugging, and deployment
- Deep practical experience integrating ML models and LLMs into production systems, including retrieval, tool use, structured outputs, and evaluation
- Ability to assess model limitations and choose methods suited to the available data
- Ability to run working sessions with clients' technical and non-technical stakeholders and identify impactful solutions
- Ability to translate complex analysis into clear conclusions for senior stakeholders and clients, including limitations of the evidence
- Hands-on experience with scalable data pipelines, automated testing, CI/CD, and cloud infrastructure, ideally AWS
- Comfortable owning application code and AI deployment
- Experience turning bespoke analyses or pilots into products used repeatedly by multiple clients or teams
- Background in life sciences, pharmaceutical analytics, or capital markets helpful but not required
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