Summary
Jineet Doshi is a Staff Machine Learning Engineer based in the San Francisco Bay Area with 12 years of experience building and scaling ML systems that drive measurable business impact across fraud, risk, retention, and conversational AI. He has led cross-functional teams at Intuit to deploy LLM-powered products and reusable ML platforms used by dozens of teams and hundreds of engineers, contributing tens of millions in identified and realized revenue. His work spans end-to-end model lifecycles—from real-time fraud prevention that cut account takeovers by 74% to RAG-backed tax assistants and NLP campaigns that boosted conversions and retention. Trained at Carnegie Mellon and experienced in large-scale data stacks (Spark, Hadoop) and IoT, he blends research rigor with production engineering, and has co-chaired a high-profile Explainable AI workshop at KDD. Known for mentorship and product-focused architecture, he emphasizes interpretability and reusability to shorten development cycles and surface unexpected user patterns.
12 years of coding experience
4 years of employment as a software developer
School, School at Bombay Cambridge School
Bachelor's Degree Double Major in Computer Science and Electronics, Bachelor's Degree Double Major in Computer Science and Electronics at Dhirubhai Ambani University
High School Electronics, High School Electronics at AAV Patel Jr College
Master’s Degree Information Technology, Master’s Degree Information Technology at Carnegie Mellon University
English, French, Hindi