Director, Applied AI Engineering - Life Sciences Markets

Vi • Remote (EST/PST/CT),

6d

About The Position

Build and lead the technical delivery of AI applications within Vi’s Life Science Markets squad. The squad transforms 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: most of the time is spent designing systems, writing production code, integrating models, and shipping AI workflows, while owning technical standards and overall squad delivery. The role leads the squad’s engineers and execution, sets implementation standards, reviews code and architecture, and coordinates technical priorities and dependencies.

The Director identifies valuable life sciences market questions for Vi’s data, prioritizes use cases, defines success measures, and turns them into a focused roadmap. The role designs and implements systems combining ML models, LLMs, retrieval, and workflow automation, balancing model quality, reliability, latency, and cost. It also owns testing, evaluation datasets, monitoring, predictive-model validation, production deployment, observability, and incident resolution, while developing reusable capabilities that scale across therapies, companies, and clients.

The position works directly with clients and domain experts to understand analytical workflows, explain findings, challenge assumptions, and translate feedback into product priorities. A background in life sciences, pharmaceutical analytics, or capital markets is helpful but not required.

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.
• Client-facing engineering ability, including leading working sessions with technical and non-technical client stakeholders and identifying what will move the needle.
• Ability to translate complex analysis into clear conclusions for senior stakeholders and clients, including explaining what the evidence cannot support.
• Hands-on depth in scalable data pipelines, automated testing, CI/CD, and cloud infrastructure, ideally on AWS.
• Comfort owning both application code and AI deployment.
• Experience turning bespoke analyses or pilots into products used repeatedly by multiple clients or teams.

Nice To Haves

• Background in life sciences, pharmaceutical analytics, or capital markets.

Responsibilities

• Personally build and ship core AI services and workflows; own critical implementation work and resolve technical blockers alongside engineers, data scientists, and domain experts.
• Lead the engineers in the squad 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, making practical choices about quality, reliability, latency, and cost.
• Build automated tests, evaluation datasets, and monitoring for AI outputs and application reliability.
• Validate predictive models rigorously, including point-in-time backtesting, baseline comparisons, and checks for leakage and coverage bias.
• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Director
• Education Level: No Education Listed