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Metro mid-level GenAI role with popular title and strong employer brand increases competition.
Deep GenAI/LLMOps skills and regulated biopharma familiarity limit cross-industry transferability.
Explicit 5+ years, mandatory GenAI production experience and regulated-domain requirements create stringent filters.
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Lead design and delivery of enterprise-scale GenAI and advanced ML solutions including LLM applications (RAG, fine-tuning, agentic workflows) from prototype to production.
Own end-to-end ML model lifecycle including problem framing, data strategy, model building, deployment, monitoring, and iteration with governance aligned to responsible AI and regulatory needs.
Provide technical leadership by setting architecture standards, mentoring junior data scientists, collaborating cross-functionally, and managing priorities and stakeholder engagement.
5+ years of applied data science/machine learning experience with strong hands-on GenAI (LLMs, RAG, fine-tuning, agentic workflows) expertise.
MS or PhD in a quantitative discipline (PhD preferred).
Experience deploying ML/GenAI solutions in regulated (e.g., GxP) or healthcare/biopharma environments preferred but not strictly mandated.
Advanced Python programming, SQL, cloud deployment (AWS or Azure), containerization (Docker/Kubernetes), and MLOps/LLMOps practices.
Senior-level individual contributor or technical lead with deep GenAI expertise and experience driving GenAI solution architecture and production implementations.
Demonstrated ability to lead technical teams, mentor junior staff, and influence architectural decisions and standards across a pod or group.
Experienced working cross-functionally with business and engineering stakeholders in regulated or complex domains (preferably biopharma or healthcare).