Associate Director, Generative Ai Evaluation & Standards
Johnson & Johnson MedTech
Cornellà de Llobregat, Spain
Hybrid
Generative ai evaluation frameworks
Llm quality and rag performance
Ai/ml evaluation in regulated environments
Johnson & Johnson MedTech is seeking an Associate Director for Generative AI Evaluation & Standards, focusing on establishing quality frameworks for generative AI in a regulated pharmaceutical R&D environment. The ideal candidate will have significant experience in AI/ML evaluation, particularly within the pharmaceutical industry, and possess strong leadership and communication skills
Job Summary
This is a newly created leadership role within the Generative AI organization, reporting directly to the Head of Generative AI.
The role owns evaluation and governance for all generative AI work across R&D, defining what quality means for generative AI in a regulated pharmaceutical R&D environment.
Build and lead the GenAI Evaluation & Standards team, attracting, developing, and retaining top talent in AI evaluation, quality science, and governance.
Matching Summary
Match Score: 85
Johnson & Johnson MedTech is seeking an Associate Director for Generative AI Evaluation & Standards, focusing on establishing quality frameworks for generative AI in a regulated pharmaceutical R&D environment. The ideal candidate will have significant experience in AI/ML evaluation, particularly within the pharmaceutical industry, and possess strong leadership and communication skills.
Skills & Requirements
Must-have
Generative AI evaluation frameworks
LLM quality and RAG performance
AI/ML evaluation in regulated environments
People leadership experience
Scientific rigor and independent judgment
Nice-to-have
Domain expertise in therapeutic areas
Influencing build-versus-buy decisions
Enterprise IT collaboration
Multi-modal AI evaluation experience
Key Requirements
Advanced degree (PhD strongly preferred)
Minimum 8 years post-academic industry experience
Hands-on expertise with generative AI systems
Demonstrated track record designing evaluation frameworks