AI Engineer Clinical Data Science

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.
Deploy and manage agents on Google Vertex AI.

Document Understanding & RAG:

Build document processing pipelines (PDFs, Word/DOCX) extraction, parsing, table detection, structure recognition.
Design and build RAG pipelines grounded in source documents.
Process, extract and transform data from unstructured and semi-structured sources.
Code Quality & Engineering Practices:
Write clean, well-tested, maintainable Python code following SOLID principles and recognised design patterns.
Apply single responsibility, dependency inversion, and interface segregation in real codebases not just theory.
Write meaningful tests and maintain high standards across the team.
Refactor and improve existing code as part of normal development workflow.

AI-Assisted Development:

Use AI coding tools (e.g. Gemini CLI, GitHub Copilot) as a core part of your development workflow.
Critically review and validate AI-generated code understanding what it produces, why, and when it's wrong.
Write effective prompts to direct AI tools toward correct, secure, well-structured output.
Know when to use AI and when to write code manually judgement over speed.

Platform & Infrastructure:

Integrate and orchestrate LLM providers available through Google Vertex AI (Gemini, etc.).
Build internal tools and applications using Stream lit and Fast API.
Containerize and deploy services using Docker.

Required Skills & Experience:

MSc in Data Science, Computer Science, Bioinformatics, or related field (or equivalent practical experience), Strong Python skills.
Hands-on experience building RAG systems or LLM-powered applications (using LangChain, LlamaIndex, or similar frameworks).
Experience integrating LLM APIs (Google Gemini, OpenAI, or similar) we work primarily through Google Vertex AI.
Working knowledge of vector databases (ChromaDB, Weaviate, Qdrant, Pinecone, or similar).
Cloud platform experience (GCP preferred, especially Vertex AI).
Docker and containerized deployments.
Strong software engineering fundamentals SOLID principles, clean code practices, design patterns, testing, version control (Git), code review.
Comfortable using AI-assisted development tools (e.g. Gemini CLI, GitHub Copilot) and critically evaluating what they produce.
Strongly Preferred.
Experience with agentic AI patterns multi-agent orchestration, tool use, autonomous workflows (LangGraph, Google ADK, or similar).
Document processing experience extracting and parsing data from PDFs and Word/DOCX files programmatically.
Understanding of LLM evaluation principles and output quality assessment (BLEU, ROUGE etc, code execution metrics, or similar).
Data science fundamentals Pandas, NumPy, scikit-learn, statistical analysis, data visualization.
Prompt engineering and optimisation techniques.
Streamlit application development.

Domain Knowledge:

Infrastructure & DevOps :

Knowledge Graphs:

AI/ML:

Experience with LLM-driven code generation.
LLM fine-tuning experience (e.g. LoRA, PEFT, RLHF, Vertex AI model tuning, or similar approaches).
NLP and text processing (HuggingFace Transformers, Sentence-Transformers).
PyTorch or TensorFlow (for custom model work if needed).
Google ADK (Agent Development Kit) or Vertex AI Agent Builder.
Model Context Protocol (MCP) for tool integration and interoperability.

Other:

What You'll Work With:

Languages: Python (primary), SQL, some TypeScript/R.
AI/ML : Lang Chain, LlamaIndex, Lang Graph, Google ADK, MCP, Hugging Face Transformers, Sentence-Transformers, Google Gemini (via Vertex AI).
Document Processing: PyMuPDF, python-docx, pdf plumber, OCR tools.
Data: Pandas, NumPy, SciPy, scikit-learn, Plotly.
Databases: Vector databases, graph databases, relational databases.
Infrastructure: Docker, Google Cloud Platform (Vertex AI, GCS), Terraform, GitHub Actions.
Applications: stream lit, Fast API, Flask.
Tools: Python packaging, testing frameworks, linting, Git.

About You:

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.

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