Meta's internal operations teams run on data at a scale most companies never see. I work on AI systems that help leadership make faster decisions — NLP pipelines that turn meeting transcripts into structured insights, a natural-language-to-SQL agent for real-time data retrieval, and OR models that optimize how resources get scheduled across facilities globally. One of those models reduced overhead costs by $2.1M annually.

CXApp builds enterprise workplace software and dashboards for Fortune 500 companies. I owned the AI layer — RAG pipelines, LLM fine-tuning, vector search, ML forecasting, and the backend infrastructure to serve it all to 100k+ weekly active users. Built on top of Qdrant, FastAPI, GKE, and Postgres. Latency target was sub-200ms. We hit it.

TruckX builds telematics software for commercial trucking fleets. I worked on ML systems for battery health monitoring and a recommendation engine that figured out where drivers should stop. The battery model ran on both lithium-ion and solar-panel chemistries. The stop recommendation system went to a 300-truck pilot and cut fuel usage by 15%.

Dream11 was India's largest fantasy sports platform — 200M users, peak traffic during IPL that would buckle most systems. I built optimization models using Gurobi (MIP) for contest structuring and team selection, churn prediction with XGBoost, and causal inference work on promotions. One A/B test I designed increased revenue 1.03% monthly.

OYO was the world's third-largest hotel chain when I joined. I built the analytics infrastructure — automated pipelines, real-time pricing algorithms, and Python bots that delivered CXO-level metrics to Slack every morning. Reduced reporting turnaround by 90%. Learned early that the difference between a useful model and a useless one is whether someone actually looks at the output.