Responsibilities: what you will drive

• Dataset Engineering & Validation: design robust pipelines for processing Electronic Health Records (EHR) and medical literature. Implement rigorous multi-stage validation frameworks (Sensitivity/Specificity analysis) to ensure clinical safety and model reliability.
• LLM Fine-tuning: adapt Large Knowledge Models using SFT, DPO, or PEFT (LoRA/QLoRA) for specialised medical domains and complex clinical diagnostic reasoning.
• Advanced RAG & Graph RAG: architect hybrid retrieval systems that combine vector databases with Knowledge Graphs to eliminate hallucinations and ensure factual grounding.
• Explainability & Interpretability: develop methods to make model outputs transparent. The engine must provide "reasoning paths" — justifying recommendations by citing specific medical evidence, clinical protocols, and graph relations.

Experience: what you bring

• GenAI Stack: expert knowledge of Transformer architectures and hands-on experience fine-tuning LLMs (Llama 3, Mistral, etc.)
• Graph ML: hands-on experience with Knowledge Graphs, Triple-stores, or Graph Databases (Neo4j, ArangoDB) and Graph Neural Networks (GNNs).
• Retrieval Systems: proficiency in LangChain / LlamaIndex and vector search engines (Pinecone, Milvus, or Weaviate).
• XAI Tools: practical experience with SHAP, LIME, or custom attention-mapping techniques for model interpretability.
• Validation & Stats: strong background in statistical validation for high-stakes environments and handling imbalanced messy data: no ground truth, no ideally ready-to-go data.
• Fine-tuning: identifying important signals and how they can be combined, catching model’s false output timely.

Perks: why join us

• Solve the "why": you won't just build a model, you'll build a system that clinicians can trust because they understand its logic.
• Cutting-edge stack: work at the absolute forefront of AI, combining LLMs with structured Knowledge Graphs (Graph RAG).
• Meaningful social impact: your work directly contributes to better patient outcomes, faster recovery, and a significant reduction in diagnostic errors.

Location

Remote

Department

IT&Infrastructure

Employment Type

Full-Time

Minimum Experience

Experienced