Lead AI Engineer at The Evidence Company | Agentic AI, LLM Systems, Production RAG & Knowledge Graphs
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Not sure they're the right fit? Run the matcher insteadLead AI engineer with over 10 years of experience in machine learning, data engineering, and agentic systems. Leads the technical direction and AI platform architecture at an Imperial College London spin-off (The Evidence Company). Designed from scratch an agentic knowledge-graph and RAG system for translating and analyzing large texts with Neo4j, LangChain, and vector embeddings. At Builder.ai, automated requirements-based documentation generation with NLP/LLM for 500+ enterprise projects. Author of open-source AI projects (MoM LLM for parallel multi-model orchestration, learners-mcp built on Model Context Protocol). Specializes in quality evaluation (evals), schema validation, hallucination guardrails, and optimizing LLM call costs.
Leading the architecture of explainable AI systems for healthcare and biopharma based on Imperial College London technologies.
Building an agentic knowledge graph and RAG pipeline for translating 90K+ word texts. Reducing API costs by 60% through caching and limit optimization.
Automating requirements documentation for 500+ enterprise projects with NLP, achieving 88% F1 accuracy.
Knowledge-graph platform for searching and matching clinical evidence with explainable source references.
RAG pipeline for translating and analyzing multi-page documents of over 90,000 words with query caching.
Tool for parallel orchestration of multiple language models with a unified query interface and answer comparison.
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