Selected work across AI engineering, data engineering, and software engineering. Each card links to the case study + code where available.
A geospatial similarity engine that learns the structural fingerprint of a place from OpenStreetMap features and returns 'what other places are like this?' across multiple countries. Custom-trained vision-transformer embeddings, pgvector, MCP server, agent-driven map UI. Built end-to-end.
Co-founded an end-to-end GenAI platform that automates the innovation workflow for Consumer Packaged Goods companies, from idea prototype through to production-ready concept. Full-stack React + Python + LLM integration, shipped to early customers.
A multi-tenant data-quality platform: a Python SDK that runs inside each team's Databricks workspace, a FastAPI + Streamlit control plane, a fact/dim PostgreSQL store for results, threshold-based Slack alerting, and a no-code UI for non-engineers. Built on Great Expectations with a custom-expectations API for team-contributed rules.
An AI-powered Slack bot that reads a data-collection-plan document on Confluence, cross-references it against the live event registry and metrics catalog, optionally analyses a GitHub PR alongside, and posts a structured pass/fail review back into the thread. Multi-agent workflow with Agno + ChromaDB vector search + deterministic result caching.
Headless plugin giving any Vercel-AI-SDK-compatible LLM (Anthropic, OpenAI, Azure, Google) conversational control over a TomTom map. Contributed to design and implementation.
Two production Model Context Protocol servers giving AI agents structured access to TomTom APIs. Both open source, both authored end-to-end (TypeScript, design + impl + docs).
Production multi-agent system for traffic analytics, demoed by Product Management at CES 2026 Las Vegas. Five specialised agents (routing, route monitoring, junction analytics, area analysis, traffic volumes) orchestrated through Agno's Team abstraction, served over AG-UI v1.0 with persistent chat memory.
Full-stack AI app that takes an address and returns a comprehensive neighbourhood profile (POIs, accessibility, routing to key destinations, market signal, livability score) by orchestrating a multi-step agent workflow over the TomTom MCP servers. Hackathon project that showcases MCP usage.
A production agentic navigation system that turns natural-language queries ('find me a quiet café between my next two meetings') into structured plans grounded in real-time location data. Full-stack, React/Next.js + Python FastAPI + Azure OpenAI, with a RAG pipeline for semantic POI search and autonomous multi-step execution planning.
Refactored a vehicle-telemetry processing pipeline. Transforms raw nested JSON / protobuf from millions of in-car navigation clients into clean Delta tables that power navigation-quality dashboards. Designed for query simplicity: PMs answer 'route success rate in country X this week' with a single SELECT.
A Pydantic-based data-validation library for the team that runs API analytics. Engineers point it at a JSON event, it generates a schema, they edit it to add field-level rules (null, range, allowed-values, custom), and the orchestrator runs validations every ingest. Distributed as an installable wheel.
Co-founded a news-aggregation platform that uses GPT models to synthesise stories from multiple sources, removing single-source bias. Plus a RAG-based study chatbot for students preparing for India's public-service exams: embeddings pipeline, vector search, and a conversational interface.
Refactored the inconsistent set of ETL pipelines feeding TomTom's API analytics into a single OOP template, distributed as an internal Python package. Engineers subclass the base, get live Azure Data Explorer connections for free, and only write the business logic in extract / transform / load. Result: consistent, version-controlled pipelines across volume, response-time, and error-rate use cases.
REST API layer that powers the analytics dashboards on developer.tomtom.com. Sits on top of an Azure Data Explorer (Kusto) backend that ingests every API call across TomTom's developer products. Volume reports, response-time percentiles, error-rate breakdowns, and per-product usage all flow through this layer.
Architected an enterprise data lake on Google Cloud (BigQuery, Cloud Storage, Cloud Composer / Airflow) at Oyo Vacation Homes. Designed a metadata-driven ETL framework in PySpark that cut new-pipeline development time by ~70%. Established schema-versioning and quality-validation patterns the team still uses.
Led ML team of 3 engineers building a content-classification + Named Entity Recognition system for ABN AMRO. Processed thousands of customer emails daily at 95%+ accuracy. End-to-end MLOps with Azure Data Factory, Databricks, and MLFlow.
A PySpark-based Python package abstracting the I/O patterns between Azure Databricks and the half-dozen sinks an enterprise pipeline typically needs. Built during the ABN AMRO Bank engagement and open-sourced internally there.
Lead engineer on an enterprise data-quality platform with a pluggable connector framework for profiling, validation, and reconciliation across heterogeneous sources. Adopted across multiple internal customer engagements.
Optimised long-running ETL pipelines for Citi Bank Singapore (TCS engagement). Cut Data Stage job execution from 4 hours to 1 hour through Python multiprocessing + PySpark parallelisation. Designed real-time message handling with Kafka, plus AVRO / Parquet conversion and HDFS compression strategies for the bank's machine-learning data pipelines.
First professional engagement: built REST APIs and backend services for Sainsbury's (UK's largest retailer) user-management platform via TCS. Python Flask, OOP design patterns, and direct collaboration with the Corporate Pingit Business Product Owners through an Agile SDLC.