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 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