Director, Applied AI Engineering – Life Sciences Markets

. Build and lead technical delivery of AI applications within Vi’s Life Science Markets squad .

Posted 11/2/2026full-timeRemote • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying AI applications, integrating ML models and LLMs, and leading technical delivery in life sciences markets. Capable of translating complex analyses into actionable insights for stakeholders while maintaining rigorous model validation and application reliability.

Highest-signal resume keywords

AI Application DevelopmentML Model IntegrationTechnical LeadershipCloud Infrastructure (AWS)Automated Testing and CI/CD

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Production Code DevelopmentSystem DesignModel EvaluationData Pipeline ManagementWorkflow AutomationPredictive-Model ValidationAPI DevelopmentIncident ResolutionBacktestingStructured Outputs

Soft Skills

Stakeholder CommunicationAnalytical Problem SolvingClient CollaborationFeedback IntegrationTechnical Ownership

Tools & Technologies

AWSCI/CD ToolsAutomated Testing FrameworksMonitoring ToolsData Integration Tools

Industry Keywords

Life SciencesPharmaceutical AnalyticsCapital MarketsAI WorkflowsTechnical Delivery

Tech Stack

Tools & technologies

AWSCloud

About the role

Key responsibilities & impact

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

What you’ll need

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