I'm an AI Engineer, Data Scientist & Data Engineer who turns complex data and LLMs into real, production-ready systems. Specialising in GenAI/RAG, machine learning, and cloud-native data engineering, I build agents, models, and pipelines that drive real business impact.
Explore My Work Get In TouchMy top 6 projects: risk analytics, forecasting, multi-agent AI, NLP, and GenAI/RAG systems.
Risk Analytics
Four risk-analytics modules pulling real, live regulatory data (openFDA, ClinicalTrials.gov, US Treasury OFAC), built on a shared ML risk-scoring framework and surfaced through an interactive Streamlit dashboard.
View on GitHubA production-grade demand forecasting system for Tesco-scale retail operations: LSTM/GRU, TCN, Transformer, N-BEATS, and DeepAR models with hierarchical reconciliation across 50 stores and 200 products.
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AI Engineering
Production-oriented multi-agent AI systems across insurance, healthcare, and fintech: LLM reasoning combined with deterministic fallback logic, CI gates, containerisation, and HTTP APIs for each domain.
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NLP
A fully local NLP platform for the BBC News archive: fine-tuned topic classification, zero-shot sub-topic tagging, named entity recognition, and semantic search, served through an API and web interface with no external calls.
View on GitHubA microservice for querying WHO guideline documents using Retrieval-Augmented Generation: PDF ingestion, SBERT embeddings, vector indexing, and semantic summarisation, with pytest coverage.
View on GitHubA medical symptom-triage assistant layering a rule engine, an ML classifier, and an LLM fallback, with SHAP-based explainability, CI, Docker, and OpenAPI docs.
View on GitHubAn AI Engineer, Data Scientist & Data Engineer with a strong background in GenAI/RAG systems, LLM agents, machine learning, and cloud data platforms (Azure, AWS, Snowflake, Databricks, Microsoft Fabric), helping organisations in finance, healthcare, and logistics turn data into decisions.
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