AI Team Lead

Ai Futures; München; Hybrid; EUR 120,000 - 190,000; Full time

Job summary

Global Pharmaceutical Company in Germany seeks an AI Team Lead to steer a cross-functional squad of ML Engineers, Data Scientists and AI Platform specialists. You will architect LLM and agentic solutions, align with GxP and EU AI Act, and deliver production-ready AI across drug discovery and clinical development.

You will mentor 4 to 8 engineers, set technical direction, evaluate build-vs-buy decisions, and ensure systems are monitored and compliant in a regulated setting.

Qualifications

• Hands-on experience building and deploying LLM-based systems.
• Proven leadership: architecture direction and mentoring engineers.
• Strong Python and MLOps fundamentals (CI/CD, monitoring).
• Experience in pharma/regulated environments is a plus.
• Ability to present to senior non-technical leaders.

Responsibilities

• Lead design and deployment of LLM-based systems and RAG pipelines.
• Define team approach to model selection and evaluation.
• Mentor 4 to 8 AI/ML engineers and drive delivery roadmap.
• Ensure compliance with GxP and EU AI Act; implement monitoring.
• Promote responsible AI with explainability and human-in-the-loop.

Skills

LLM Deployment

Technical Leadership

Python & MLOps

Regulated Industry Experience

Executive Communication

Fluent English

Job description

Global Pharmaceutical Company | Germany | Hybrid | Permanent

Want to lead an AI team where the work could help bring new medicines to patients faster? This is a hands-on leadership role at one of the world's leading research-driven pharmaceutical companies, building LLM and agentic AI systems across drug discovery and clinical development.

About the Company

A global, research-driven pharmaceutical company operating across 100+ markets, with a pipeline spanning small molecules, biologics and precision medicine. AI is a core part of the company's long-term strategy, backed by dedicated AI teams, academic partnerships and a clear commitment to deploying AI responsibly in a GxP-regulated environment.

The Role

As AI Team Lead, you will own the technical leadership of a cross-functional AI squad working on high-priority use cases across the drug development lifecycle. You will set direction for a team of ML Engineers, Data Scientists and AI Platform specialists while staying close to the code.

You will architect LLM and agentic solutions, make key build-vs-buy decisions and take systems into production to the standard a regulated environment demands. You are the bridge between AI research ambition and engineering reality.

What You Will Do

Technical Leadership

• Lead the design and deployment of LLM-based systems, RAG architectures and agentic AI workflows across research and clinical use cases
• Define the team's approach to model selection, prompt engineering, fine-tuning and evaluation

Team & Delivery

• Lead and mentor a team of 4 to 8 AI and ML Engineers, running technical reviews and building a high-performance culture
• Own delivery across the team's roadmap, balancing exploration with production-grade execution

AI in a Regulated Context

• Ensure AI systems align with GxP guidelines, data governance requirements and the EU AI Act
• Implement model monitoring, drift detection and retraining pipelines for high-stakes decision support
• Champion responsible AI: explainability, bias assessment and human-in-the-loop design

What You Bring

• Hands-on experience building and deploying LLM-based systems, RAG pipelines and agentic AI applications in production
• Proven technical leadership: setting architecture direction, reviewing code and mentoring engineers
• Strong Python and MLOps fundamentals (versioning, monitoring, CI/CD)
• Experience in or alongside a regulated industry such as pharma, medtech or financial services
• Confidence presenting to senior non-technical leaders without losing depth with engineers
• Fluent English; German is a plus but not required

Nice to Have

• Life sciences AI use cases such as literature mining, clinical NLP or regulatory document intelligence
• Knowledge of GxP validation frameworks (GAMP5, 21 CFR Part 11, EU Annex 11)
• Experience with knowledge graphs or biomedical ontologies (MeSH, SNOMED, Gene Ontology)
• A track record of taking an AI team from proof of concept to sustained production delivery