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ML Engineering Leadership, Siri at Apple
Currently an ML Engineering leader in Siri. Prior to Siri, I was part of the Camera & Photos organization at Apple, where I led the Photos AI team. My team was responsible for all of the AI and ML work in Photos that spanned both algorithm development (e.g. adapting large language models for photos specific use cases) and the on-device implementation of the intelligence infrastructure for photos features. As one of the key DRIs from photos, I have been responsible for shipping both custom memories movies in iPhone 16 Pro ( https://youtu.be/EcHUOKPDMFU?si=hfZilds1mp546B5N ) as well as natural language semantic search in Photos. Previously, I worked as the Head of ML and Data at Epic, where my team’s mission was to leverage data to build awesome data products and intelligent, personalized systems that helps every child, parent, and teacher discover the book they want on Epic for the children to become better readers. ML and Data at Epic was organized into 3 main areas: 1) ML Science and Engineering of ML, Deep Learning, and Knowledge Graph algorithms for personalized book recommendations, search, and features that assisted the development of a child’s reading ability, 2)Data Engineering, and 3) Data Analytics. I built and led a team of 24 people, which included 2 mangers, 4 tech leads, and 1 program manager. Prior to Epic!, I was a Director of AI at NIO where I built and led AI science and software engineering teams working on prediction, decision making, and rare event detection for self driving cars. I built the Trajectory Prediction, Decision Making, and Rare Event Detection teams comprised of AI Scientists, cloud and embedded software engineers from scratch. Previous industry experience also includes companies like Pivotal, Philips Healthcare, and Oracle. I hold a Ph.D. degree from The University of Texas at Austin, where I studied image processing and co
The University of Texas at Austin
M. S, Biomedical Engineering
January 1, 2007 – January 1, 2009
The University of Texas at Austin
Ph.D., Biomedical Engineering
January 1, 2007 – January 1, 2012
RV College Of Engineering
B. E, Electronics and Communication Engineering
January 1, 1998 – January 1, 2002
Apple
Machine Learning Engineering Leadership, Siri
November 1, 2025 – Present
Apple
Machine Learning Engineering Leadership, Photos
September 1, 2022 – October 1, 2025
Epic for Kids
Head of Machine Learning and Data
May 1, 2019 – September 1, 2022
NIO
Director of Artificial Intelligence
September 1, 2017 – April 1, 2019
NIO
Principal Engineer - Data Science
June 1, 2016 – August 1, 2017
Pivotal, Inc.
Senior Data Scientist
February 1, 2014 – May 1, 2016
VuCOMP (now iCAD)
Algorithm Engineer, Research and Development
January 1, 2013 – February 1, 2014
Plano, TX
Los Alamos National Laboratory
Summer Intern
July 1, 2009 – August 1, 2009
Loas Alamos, New Mexico
The University of Texas at Austin
Graduate Research Assistant
August 1, 2007 – December 1, 2012
Philips
Technical Lead
July 1, 2003 – July 1, 2007
Bengaluru, India
Oracle
Applications Engineer
July 1, 2002 – July 1, 2003
Hyderabad, India
Cultural Fit Analysis
The candidate's career trajectory shows a strong focus on Machine Learning and Data Science leadership roles across various industries (consumer tech, automotive, education tech, medical imaging). While the target role is 'Data Analyst', the candidate's experience is heavily skewed towards senior leadership in ML Engineering and Data Science, which might indicate an overqualification or a mismatch in day-to-day responsibilities for a pure analyst role. The breadth of experience is significant, but the depth is primarily in ML/AI system design and leadership rather than traditional data analysis.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership, team-building, and project delivery skills. Their experience in leading diverse technical teams and shipping complex ML products suggests excellent operational fit for roles requiring strategic technical direction and execution. The detailed descriptions of initiatives and outcomes indicate strong communication of technical achievements.