Sr. Applied AI Engineer, Life Science Markets

VI; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Vi is seeking a Sr. Applied AI Engineer to join its Life Science Markets squad in a fully remote role across EST/PST/CT.

You will design and deploy AI-powered applications, services, and data workflows, collaborating with engineers, data scientists, and domain experts to move from prototype to production. You will work on ML models, LLMs, retrieval, and workflow automation to answer life sciences market questions, with strong emphasis on reliability, latency, and cost.

Qualifications

• Experience shipping applied AI applications from prototype to production.
• Experience integrating ML models and LLMs into software, including retrieval and structured outputs.
• Client-facing engineering: run sessions with clients and translate needs into priorities.
• Strong software engineering fundamentals: API design, testing, version control, and maintainable code.

Responsibilities

• Design and implement systems combining ML models, LLMs, retrieval, and workflow automation.
• Build automated tests, evaluation datasets, and monitoring for AI outputs and application reliability.
• Production ownership: deploy and operate APIs, integrations, deployment, observability, and incident resolution.
• Collaborate with clients and domain experts to understand workflows and turn feedback into product priorities.

Skills

Applied AI development

API design

Testing

Version control

CI/CD

Model deployment

Tools

AWS S3

AWS Glue

AWS EMR

MWAA

SageMaker

Job description

# Sr. Applied AI Engineer, Life Science MarketsRemote (EST/PST/CT)#### About The PositionJoin Vi's Life Science Markets squad that turns large, varied proprietary datasets into products that help evaluate pharmaceutical markets, treatment adoption, competitive dynamics, and drug commercial performance.You will design, build, and deploy AI-powered applications, services, and data workflows, working hands-on with engineers, data scientists, and domain experts. You will own implementation from prototype through production. A background in life sciences or capital markets is very helpful, but not required.****What You'll Own***** Applied AI development: 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 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 track record of independently shipping applied AI applications or services, from initial prototype to production use.* Practical experience integrating ML models and LLMs into software, including retrieval, tool use, structured outputs, and evaluation. Sound judgment about model limitations and when a simpler approach is sufficient.* 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.* Strong software engineering fundamentals: API design, testing, version control, and maintainable code.* Experience deploying and operating AI systems, including CI/CD, model versioning, monitoring, and troubleshooting.* Ability to work with large datasets in the cloud, ideally using AWS services such as S3, Glue, EMR, MWAA, and SageMaker.* Sound judgment with incomplete or noisy data, clear communication, and the ability to work independently within a cross-functional squad & client stakeholders.* A background in life sciences, pharmaceutical analytics, or capital markets is very helpful, but not required.