MSc in Data Science, Computer Science, Bioinformatics, or related field, or equivalent practical experience.
Strong Python skills with hands-on experience in RAG systems or LLM-powered applications.
Experience integrating LLM APIs, particularly using Google Vertex AI and relevant frameworks.
Working knowledge of vector databases like ChromaDB and Weaviate.
Experience in cloud platforms, preferably GCP and containerized deployments using Docker.
Responsibilities
Develop generative AI and automation tools for clinical reporting workflows.
Build AI-driven code generation pipelines and quality assessment frameworks.
Design and implement human-in-the-loop review workflows to enhance output quality.
Research and evaluate new AI methodologies and frameworks for specific tasks.
Deploy and manage multi-agent systems for data workflows through orchestrated AI processes.
Benefits
Join a pioneering team in a major pharmaceutical organization focused on transformative AI solutions.
Work hands-on with cutting-edge generative AI technology and tools.
Opportunity to continuously learn and stay updated in a rapidly evolving field.
Full Job Description
Job Description:
We are looking for an AI Engineer to join our Data Science team, building AI-powered solutions for clinical data processing and analysis within a major pharmaceutical organization. You will design, develop and deploy generative AI systems that automate clinical reporting workflows, extract intelligence from documents, and accelerate data-driven decision making.
This is a hands-on engineering role - you'll be writing production code, not just building prototypes.
Responsibilities:
Generative AI & Automation.
Develop LLM-powered automation tools for clinical reporting and document generation workflows.
Build AI-driven code generation pipelines and quality assessment frameworks.
Design and implement human-in-the-loop review workflows with feedback loops to continuously improve output quality.
Research & Evaluation:
Research and evaluate emerging AI methods, frameworks, and techniques for specific tasks - e.g. comparing fine-tuning vs zero-shot approaches, assessing new document extraction tools, or trialling new agentic frameworks.
Prototype and benchmark new approaches before recommending adoption.
Stay current with a rapidly evolving field and bring new ideas to the team.
Agentic AI & Orchestration:
Design and build multi-agent systems for data workflows - agents that retrieve, generate, validate, and iterate autonomously.
Implement agent orchestration using frameworks such as Google ADK, Lang Graph, or Lang Chain.
You care about code quality - not just making things work, but making them maintainable.
You're comfortable working across the full stack of an AI application, from data ingestion to user-facing tools.
You can context-switch between multiple projects and work autonomously.
You're curious about the clinical/pharmaceutical domain and motivated to learn it.
You see AI-assisted development as a force multiplier, not a replacement for engineering judgment.
You're a self-directed learner who researches new methods and tools, evaluates them critically, and knows when to adopt vs when to stick with what works.
