Director, Applied AI Engineering - Life Sciences Markets

Vi; United States; A distancia; USD 180.000 - 260.000; Jornada completa

Descripción de la vacante

Vi is seeking a hands-on leader to build and shepherd AI applications in its Life Science Markets squad. You will design systems, write production code, integrate models, and ship AI workflows, owning technical standards and squad delivery.

A background in life sciences or capital markets helps but is not required. You’ll personally ship core AI services, lead engineers, define success measures, and turn use cases into a focused roadmap, balancing model quality, reliability, latency, and cost

Formación

• Hands-on experience building and shipping production AI services.
• Proven ability to lead engineers, set standards, review code and architecture.

Responsabilidades

• Hands-on delivery: build and ship core AI services and workflows with engineers, data scientists, and domain experts.
• Squad leadership & management: lead engineers in the squad, set implementation standards, and coordinate priorities.
• Strategy and roadmap: identify life sciences questions and turn them into a focused roadmap.
• AI architecture and implementation: design systems combining ML models, LLMs, retrieval, and automation.
• Engineering and evaluation quality: build automated tests and monitoring for AI outputs and model validation.
• Production ownership: take prototypes to production, manage models, APIs, deployments, and observability.
• Client partnership & business logic: work with clients to understand workflows and translate feedback into priorities.

Conocimientos

Production AI apps ownership

Technical leadership

ML/LLM integration

Client-facing communication

Cloud & CI/CD

Herramientas

AWS

CI/CD tooling

Descripción del empleo

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 & business logic: Work directly with clients and domain experts to understand their analytical workflows, explain findings, challenge assumptions, and turn feedback into product priorities.

What We’re Looking For

• A 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: You can run a working session with a client’s technical and non‑technical people and come out of it with what will move the needle for them. Ability to translate complex analysis into clear conclusions for senior stakeholders and clients, including what the evidence cannot support..
• Hands‑on depth in scalable data pipelines, automated testing, CI/CD, and cloud infrastructure, ideally on AWS. Comfortable owning both application code and AI deployment.
• Experience turning bespoke analyses or pilots into products used repeatedly by multiple clients or teams.
• A background in life sciences, pharmaceutical analytics, or capital markets is very helpful, but not required.