Agentic AI / LLM Engineer – Pharmaceutical R&D will lead rapid AI prototyping and predictive‑model development for the DAISAE platform within the RDE 9 program. The role bridges advanced machine‑learning techniques with multi‑modal biomedical data to deliver agentic AI capabilities that enhance drug safety risk prediction across the product lifecycle. This position collaborates with global matrix teams of data scientists, safety scientists, and digital technology partners to translate innovative prototypes into production‑ready solutions.

Key Responsibilities

• Rapidly explore, evaluate, and prototype emerging AI tools and agentic capabilities, focusing on MCP tool development and AI‑agent integrations for the DAISAE platform.
• Design, build, and refine predictive models using multi‑modal biomedical datasets—including omics, clinical trial data, real‑world data, and in‑vitro/in‑vivo studies—to support risk prediction from early‑molecule assessment through post‑marketing safety.
• Translate feasibility findings and prototype outcomes into detailed architectural designs and platform requirements for shared data foundations such as ontologies and knowledge graphs.
• Collaborate in an agile environment to prototype reusable technical components before handing them to the Client Digital Tech (RDT) team for formal validation, compliance, and production rollout.
• Partner with safety scientists, data scientists, and RDT technical leads to ensure technical execution aligns with business priorities and regulatory standards.
• Maintain an “AI native” problem‑solving mindset, staying current with fast‑moving frameworks and best practices in machine learning and LLM engineering.
• Document prototype results, contribute to governance processes, and communicate technical concepts clearly to both technical and non‑technical stakeholders across the global organization.

Requirements

• MSc in Data Science, Computer Science, Computational Biology, Bioinformatics, Biostatistics, Epidemiology, or a related quantitative field (PhD preferred).
• Minimum 2 years of hands‑on experience with biomedical datasets (omics, clinical trials, real‑world data, or in‑vitro/in‑vivo data).
• Strong expertise in predictive modeling, machine learning, and AI prototyping, with a proven track record of developing agentic AI systems and LLM tools.
• Proficiency in software‑engineering practices tailored for data‑science workflows, including agile prototyping, version control, and CI/CD pipelines.
• Excellent communication skills and a proactive attitude suitable for collaboration within a global matrix team environment.
• Preferred background in pharmacovigilance, drug safety, toxicology, or quantitative risk management is highly desirable.

What We Offer

• Competitive salary package with performance‑based incentives.
• Comprehensive health, dental, and vision benefits, plus generous paid time off.
• Opportunities for continuous learning, professional development, and support for advanced certifications.
• Dynamic, inclusive work culture that encourages innovation, cross‑functional collaboration, and global impact.