Projects
Selected AI engineering work with a focus on real-world impact, reliability, and responsible deployment.
These are representative projects across LLMs & RAG, ML engineering, and data-driven applications. Where code or demos are private, I describe the architecture, constraints, and outcomes.
Featured Case Studies
RAG Assistant for Organisational Knowledge
Built a retrieval-augmented assistant that answers questions from internal documents with source citations and structured outputs for downstream automation.
Highlights: chunking + embeddings, vector search, guardrails, evaluation set, latency-focused API design.
Predictive Modelling & Model Monitoring
Designed training pipelines and evaluation workflows to improve model performance and reliability across multiple datasets and problem settings.
Outcome: improved predictive performance through tuning + validation and better feature handling.
AI for Perinatal Mental Health (PhD Research)
Developing privacy-conscious AI methods for mental health intervention support, including conversational systems and data-driven triage concepts for real-world clinical constraints.
Focus: responsible AI, privacy preservation, and translation into practical care settings.
Production Web Apps & Cloud Deployment
Delivered full-stack applications with secure APIs, database integration, and cloud deployments with reliability and cost-aware operations.
Highlights: auth + data modelling, CI/CD, containerisation, and cloud-native observability patterns.
What I build
LLM Applications
ML Engineering
Responsible AI
Want a project like this?
Tell me the domain, the data you have, and what success looks like. I can help you ship something reliable.
Start a Conversation