AI/ML Engineer
Building intelligent systems that transform data into real-world impact. Specializing in RAG systems, semantic search, and LLM-powered applications.
AI / ML
Computer Engineering graduate (Honours in AI/ML) and a working AI/ML Engineer with hands-on experience building production-grade RAG systems, semantic search pipelines, and LLM-powered applications. I've deployed real projects using vector databases, transformer models, and cloud infrastructure.
I'm passionate about building intelligent, scalable applications — from AI Agents and RAG systems to semantic search pipelines and NLP projects. I enjoy transforming data into real-world impact and continuously learning modern AI tooling.
I graduated in Computer Engineering with Honours in AI/ML from St. Francis Institute of Technology and currently work as a Junior AI Engineer at ComplAIBridge, shipping document-AI and compliance backends. Already solving real business problems in production, I'm now looking for better opportunities to take on bigger challenges and grow my impact at scale.
AI infrastructure that gives customer-service agents in regulated industries a long-term memory and a safe, auditable way to make decisions — kept isolated per client, with a rule-based approval queue for sensitive actions. 543 tests passing.
A fraud-detection system for card payments, built as a full MLOps pipeline — it trains an XGBoost model, watches live data for drift, and automatically retrains itself when the patterns shift. Comes with a live dashboard for monitoring.
A movie recommender that searches 9,000+ films by meaning — describe a plot or upload a poster and it finds similar movies in under 100ms, matching on both story and visuals.
Reads architecture diagrams for you — finds the boxes, labels, and arrows automatically, then exports the whole structure as annotated images and clean JSON/CSV.
Reads 10,000+ restaurant reviews and turns them into insights — gauging sentiment, sorting complaints into categories, and letting you chat with a bot that answers questions about the reviews in under a second.
Upload a PDF and ask questions about it — the app finds the relevant passages and gives you grounded, source-backed answers instead of guesses.
Predicts stock prices with an LSTM model and pairs the forecast with live news and sentiment, so you get the full picture before making an investment decision.
An AI agent that reviews your code — it spots bugs, security holes, and slow spots, then hands back fixes with severity ratings and the exact lines to change.
A personal-finance app that scans your receipts, auto-sorts your spending into categories, flags unusual charges and forgotten subscriptions, and forecasts your cash flow — plus risk-profiled investment tips. Every feature is benchmarked against a frontier LLM and served from a secure, multi-tenant FastAPI backend.
An open-source card-fraud detector I shipped Phases 1–7 of — feature engineering, model face-offs, and a head-to-head against a frontier LLM, ending with a CatBoost model that hit F1 = 1.000 while running ~240,000× faster and ~45,000× cheaper.
Search fashion products by image — I built the visual-search path using CLIP embeddings plus a color histogram, tuned over 300 trials. The surprise finding: a tiny 48D color feature beat a 2048D ResNet50.
Predicts a drug molecule's properties straight from its structure — I shipped Phases 4–7, including a 3-model ensemble that matches a graph neural network and a SHAP-vs-LIME study of why the model makes each call.
Predicts which patients are likely to be readmitted to hospital — I shipped the dataset and EDA, the feature engineering, per-subgroup risk thresholds, and a SHAP deep dive into what actually drives the risk.
Classifies the clauses inside legal contracts — I shipped Phases 3–7, where a tuned LightGBM blend reached macro-F1 0.72, beating both RoBERTa-large and a frontier LLM.
I'm currently looking for better opportunities in AI/ML engineering. Whether you have a question, a project idea, or just want to say hi, I'd love to hear from you.