I build production AI systems - RAG pipelines, recommendation engines, and GenAI infrastructure - that go beyond proof-of-concept into reliable, measurable, scalable software. across fintech, retail analytics, and healthcare, working at the boundary between ML research and engineering discipline.
Dunnhumby is a global customer data science company serving major retailers including Tesco, Kroger, and Meijer. My remit covers the full pipeline from raw data and LLM architecture to production deployment and ongoing evaluation. Enterprise-scale work: large internal knowledge bases, multiple teams consuming AI outputs, and reliability expectations that make experimentation-mode thinking inadequate.
Built and shipped three end-to-end AI products independently across healthcare, legal, and fintech - each with a deliberate evaluation methodology before claiming results. Self-directed accountability to hard metrics without the safety net of a team to validate assumptions.
Signzy provides AI-powered KYC and financial onboarding infrastructure to banks and fintechs. First engineering role on live transaction pipelines where production errors have direct financial consequences.
Natural language querying over thousands of medical documents, returning cited, verifiable answers grounded in the source corpus. Core challenge: eliminating hallucination without sacrificing coverage or latency.
Two-tower dense retrieval for fintech - users and items as dense vectors via ANN. Solved cold-start where collaborative filtering failed. Redis-cached embeddings, sub-50ms P99 latency.
Map-reduce LLM summarisation with clause-level semantic evaluation. A missed clause is a liability. Preservation measured via cosine similarity, calibrated on 50 human-labelled examples.
Stock price forecasting with Kafka streaming and Kubernetes autoscaling; AQI prediction covering 300+ Indian locations with real-time dashboards. Both cross-validated and deployed.
B.Tech, Computer Science & Engineering
Modern Institute of Technology & Research Centre, Alwar
2018 – 2022 · CGPA 8.95 / 10
CGPA 8.95 within the top percentile of the graduating cohort. Strong foundation in algorithms, data structures, applied mathematics, and distributed systems.
Degree recognised at H+ level on the German Anabin database. Qualifies for EU Blue Card in Germany, Global Talent Stream(GTS) and Express Entry in Canada and Skilled Worker Visa across the EU and UK. Open to relocation. Visa sponsorship required.
A complete walkthrough of building a 94%-accurate AQI system for 300+ Indian locations - feature engineering, model selection, and what working with real government monitoring data actually looks like.
Read on Medium →What a successful RAG demo hides - and what breaks the moment someone asks about real company data. A production walkthrough from architecture to the evaluation step most teams skip entirely.
Read on Towards Data Science (TDS) →A post-mortem on a specific chunking failure in production - what went wrong, why it passed testing, and what the fix actually looked like. The kind of thing you only learn by shipping, not by reading docs.
Read on Towards Data Science (TDS) →Actively looking for senior AI or Gen AI Engineering roles in Germany, Canada, the Netherlands, the UK, and the EU broadly. Happy to talk through relocation timelines, visa support, and what the day-to-day looks like before either of us commits.