Available for new roles — 2026
Building production-grade LLM systems, RAG pipelines, and GPU-accelerated inference backends that ship with measurable impact on latency, cost, and reliability.
01 / About
I'm a Machine Learning Engineer based in Mumbai, India. My work lives at the intersection of LLM systems, NLP pipelines, and production inference infrastructure — where the hard problems aren't just accuracy but latency, cost, and reliability at scale.
I care about the parts of ML that get underspecified: ordering of components, failure modes, safety constraints, and the tradeoffs that only become clear when real traffic hits a system.
02 / Skills
03 / Experience
Tata Consultancy Services · Mumbai, India
Augurs Technologies · Lucknow, India
04 / Projects
Real-time transcription and structured clinical note generation from noisy Hindi/Hinglish doctor–patient conversations. Entity-first pipeline mitigates hallucinated clinical facts.
Conversational chatbot that translates natural language to safe, validated SQL across multiple schemas. Context-aware multi-turn memory with schema-aware routing and pronoun/reference resolution.
End-to-end retail pricing platform combining synthetic data generation, rule-based heuristics, and ML-driven price optimization with hard business safety constraints and full operational observability.
Structured meeting intelligence system extracting decisions, action items, and accountability from multi-speaker conversations. Constrained extraction as an alternative to generic abstractive summaries.
Full-stack ML application using a TCN Transformer achieving 98% R² on stock forecasting. Real-time prediction pipeline handling thousands of data points per second with 40% latency reduction.
Personalized 1RM prediction app trained on your own workout history. Full-stack FastAPI + vanilla JS with XGBoost model, confidence bands, and Epley baseline for lifter-specific strength insights.
05 / Certifications
Amazon Web Services · Nov 2024
Amazon Web Services · Mar 2025
Harvard University / edX · Oct 2025
Harvard University / edX · Dec 2025
Microsoft · Dec 2023
Python Institute · Aug 2022
06 / Contact
Open to full-time roles, contract work, and interesting LLM / MLOps challenges.