Ankur Garg is a seasoned ML/NLP engineer with 12 years of experience building production-grade conversational AI and agentic systems from research prototypes to large-scale deployments. Currently at Microsoft AI, he focuses on function-calling, synthetic data pipelines, and RL-driven improvements to tool invocation, after leading key features and model-rollout infrastructure for Gemini at DeepMind and Google. He has a strong track record of improving latency, release cadence, and NLU quality for Assistant-scale systems, plus patents and peer-reviewed publications from prior research roles at Adobe and UT Austin. Based in San Francisco, he blends deep technical rigor with product-focused engineering—architecting proxy-to-production evaluation metrics that ensure lab gains translate to real-world reliability. Ankur’s background in both academic research and applied infrastructure gives him a rare ability to close the loop between model training, synthetic trajectory generation, and robust deployment.
12 years of coding experience
8 years of employment as a software developer
Masters, Computer Science, Masters, Computer Science at The University of Texas at Austin
Indian Institute of Technology Roorkee
High School, Non Medical (Physics, Chemistry and Maths), High School, Non Medical (Physics, Chemistry and Maths) at Mount Carmel School
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