Sr. Applied AI Engineer, Life Science Markets

Vi • Remote (EST/PST/CT),

6d

About The Position

Join Vi’s Life Science Markets squad, which turns large, varied proprietary datasets into products that help evaluate pharmaceutical markets, treatment adoption, competitive dynamics, and drug commercial performance. Design, build, and deploy AI-powered applications, services, and data workflows while working hands-on with engineers, data scientists, and domain experts. Own implementation from prototype through production. A background in life sciences or capital markets is helpful but not required.

Requirements

• Demonstrated ability to independently ship applied AI applications or services from prototype to production use.
• Practical experience integrating machine learning 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.
• Ability to lead working sessions with clients’ technical and non-technical stakeholders and identify what will make a meaningful difference for them.
• Strong software engineering fundamentals, including 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; AWS services such as S3, Glue, EMR, MWAA, and SageMaker are preferred.
• Sound judgment when working with incomplete or noisy data, clear communication, and ability to work independently within a cross-functional squad and with client stakeholders.

Nice To Haves

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

Responsibilities

• Design and implement systems combining machine learning models, LLMs, retrieval, and workflow automation to address life sciences market questions.
• Make practical trade-offs among model 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 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: Senior
• Education Level: No Education Listed