Software engineer with 7+ years of product experience, now focused on applied ML, NLP, and LLM systems. Currently completing the University of Cambridge Data Science & AI programme.
End-to-end retrieval-augmented generation system over a curated ML/AI knowledge base of 2,747 chunks and ~1.07M tokens, with source previews and refusal behaviour to reduce hallucination.
Multi-stage supervised learning workflow predicting dropout risk across three stages of the student journey, framed around practical intervention timing and business-facing recommendations.
Unsupervised anomaly detection for ship engine sensor data in a predictive-maintenance context, comparing IQR, One-Class SVM, and Isolation Forest with PCA for visual analysis.
Transaction-level clustering workflow with feature engineering, K-means, PCA, and t-SNE. Five-cluster solution selected via silhouette analysis, translated into actionable business recommendations.
Open to AI engineering internships and roles focused on turning real-world data into reliable automation and decision-support tools.
Data Science with Machine Learning & AI Career Accelerator — currently in advanced NLP module, employer-led capstone due July 2026.
Available for AI engineering positions from mid-2026.