Director of AI Engineering – Life Sciences (Remote)

VI; United States; Remoto; USD 180.000 - 250.000; Tempo pieno

Descrizione del lavoro

VI seeks a Director, Applied AI Engineering – Life Sciences Markets to build and lead the technical delivery of AI applications. You will own architecture, production-grade code, and end-to-end deployment across life sciences datasets and client workflows.

You will lead engineers, define standards, and collaborate with clients to convert domain questions into scalable AI solutions using ML models, LLMs, retrieval, and automation. This role demands hands-on execution and strategic sash.

Competenze

• Hands-on delivery of production AI services and workflows.
• Experience leading engineers and setting implementation standards.
• Ability to translate client requirements into product priorities.
• Deep practical experience integrating ML models and LLMs into production systems.
• Familiarity with scalable data pipelines and cloud infrastructure.
• Excellent communication with senior stakeholders.

Mansioni

• Lead engineers in the squad and orchestrate delivery.
• Define strategy and roadmap for life sciences AI initiatives.
• Design and implement systems combining ML models, LLMs, retrieval, and automation.
• Ensure reliability, testing, and monitoring of AI outputs.
• Collaborate with clients and domain experts to shape product priorities.
• Own production deployment, observability, and incident response.

Conoscenze

Production AI

Technical leadership

ML models

LLMs

RAG pipelines

CI/CD

AWS

Stakeholder comms

Strumenti

AWS

CI/CD tooling

ML frameworks

GCP/Azure

Descrizione del lavoro

VI seeks a Director, Applied AI Engineering – Life Sciences Markets to build and lead the technical delivery of AI applications. You will own architecture, production-grade code, and end-to-end deployment across life sciences datasets and client workflows.

You will lead engineers, define standards, and collaborate with clients to convert domain questions into scalable AI solutions using ML models, LLMs, retrieval, and automation. This role demands hands-on execution and strategic sash.