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 (Google Cloud Platform 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 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:

·       Frontend experience (React, TypeScript)

·       FastAPI or Flask REST API development

PostgreSQL or similar relational databases