I build software, data, and AI systems that hold up in production — reliable backends, measurable pipelines, and applications that actually ship.
I'm an engineer working across AI, software, and data science, with an MS in Data Science from Stony Brook University and 2+ years of enterprise software experience at Genpact building Salesforce CRM architectures for global clients.
My focus is building systems that are measurable and production-ready — whether that's a backend service, a data pipeline, or an LLM application. I've shipped evaluation frameworks benchmarking GPT-4, Gemini, and LLaMA across normalized metrics, edge-native apps with recursive failover, real-time serverless dashboards, and autonomous multi-agent systems. I care about the parts that decide whether software survives contact with production: clear metrics, sane architecture, and reliability under load.
Backend services, data pipelines, and AI systems — each with metrics to back it up.
Unified DeepEval pipeline benchmarking GPT-4, Gemini 2.5, and LLaMA 3-70B across 7 normalized metrics — revealing 96% GPT-4 accuracy and 40x Gemini cost-efficiency for dynamic routing.
Vision-based system translating hand gestures into live MIDI at 30 FPS with <80ms latency, plus an LLM-powered adaptive tuning module that speeds up learning by 40%.
ARMA AI Labs — California, USA
Genpact — India
TannMann Foundation — Remote