ML / AI Engineering — Kiran Kumar
I spent a PhD asking that about human attention. Now I ask it about LLM routing, marketing attribution, and the production ML systems that answer it at scale — most recently at Chewy, EquipmentShare, and Convoy.
Currently building
A common-interface evaluation harness for comparing agentic LLM routers — LiteLLM, RouteLLM, vLLM Semantic Router — across live benchmarks. Scores on a deployment-focused metric suite (cost, latency, tool-call accuracy, Pareto frontier) instead of a single leaderboard number.
View on GitHub →A Bayesian media-mix-modeling package for measuring marketing effectiveness across geographies and KPIs at once — hierarchical geo×KPI pooling, adstock/saturation curves, and lift calibration from real incrementality experiments.
View on GitHub →Experience
Marketing measurement and causal MMM/incrementality tooling behind $500M+ in annual media spend; architected an LLM/agent prototype that helped set the org's AI roadmap.
Architected an LLM-powered RAG search & recommendation engine and a production LLM gateway on Kubernetes handling 10K+ daily requests at 99.9% uptime.
Built a cost-forecasting platform on Tab Transformer foundation models and adapters; prototyped a human-in-the-loop SHAP-based explainability loop that lifted revenue 3%.
Defined and shipped AI-driven renewal underwriting, cutting loss ratio 5%.
Improved vehicle-failure prediction recall 12% through telematics feature engineering across multiple automotive OEM product lines.
Led Industry 4.0 and explainability initiatives across operations, finance, and procurement, including an NLP-driven order-status chatbot.
PhD research on visual attention — the throughline for the routing and attribution work above. See Research.
Full role history, metrics, and skills in the CV (PDF) →
Research · 2013–2019
Plus 3 conference presentations at the Cognitive Science Society and CEMS · Google Scholar →
Contact
krankumar@gmail.com