Ipshita Chatterjee
Head of Learning @Girlswhoml
Signup · Get unlimited contacts
WORK HISTORY
Head of Learning @Girlswhoml
Bridging ML education barriers globally
EDUCATION
Convent of Jesus And Mary, New Delhi
Non Medical Sciences (Computer Science)
Netaji Subhas Institute of Technology
Bachelor of Engineering (B.E.), Computer Engineering
University of Oxford
Master of Science - MS, Computer Science
SKILLS
ABOUT IPSHITA CHATTERJEE
Senior AI/ML Research Engineer | Tech Lead, LLM Research-to-Production Workflows @ Amazon AGI | MSc Computer Science, University of OxfordI sit at the critical intersection of frontier AI research and hyper-scale engineering. As a research-to-production tech lead, I specialise in translating complex scientific breakthroughs into robust, multimodal AI products. At Amazon AGI, I co-lead the architectural design and strategic roadmap for a greenfield ML service powered by Amazon Nova Sonic - a flagship initiative defining the next generation of audio and speech AI.I focus on solving the \"velocity gap\" in generative AI: Scalable Inference: Architecting high-throughput, low-latency LLM/Multimodal serving stacks that bridge the gap between experimental models and production-grade reliability. I specialise in optimising the hand-off between foundation model training and inference-time optimisation, including quantisation and distillation Strategic Leadership: Managing cross-functional roadmaps between applied science, engineering and product leadership to eliminate friction in the model-shipping lifecycle and influence long-term technical strategy Systemic Efficiency: Reduced productionization timelines by 65% and cut model shipping bottlenecks by 99% through standardised engineering frameworks and automated evaluation tools (reducing subjective eval from 2 days to 2 minutes).With 7+ years of experience across Amazon and Adobe, I combine the technical rigour of an Oxford CS background with the pragmatism required to serve multimodal LLMs and Responsible AI (RAI) at a global scale.I am interested in opportunities where I can lead the bridge between research and production for frontier AI systems. My interests lie in scaling multimodal LLMs, high-performance inference optimisation, and architecting the end-to-end ML lifecycles that turn exploratory science into global-scale generative experiences.
This profile is compiled from publicly available professional sources. Unifers is not affiliated with or endorsed by LinkedIn. Request removal of this profile.