AI Engineer

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

Nice to Have

----------------

Domain Knowledge

Clinical trials or pharmaceutical industry experience

Familiarity with clinical data standards

Awareness of regulatory and data privacy requirements in life sciences

Infrastructure & DevOps

Terraform or infrastructure-as-code experience

CI/CD pipeline design (GitHub Actions or similar)

Knowledge Graphs

Neo4j, Cypher query language

NetworkX for graph analytics

Graph-based RAG or knowledge extraction

AI/ML

Experience with LLM-driven code gener