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Techno-Optimist | AI Engineering Leader
I am a technology & engineering executive with strong track record of building consumer and enterprise AI/ML products for over a decade. With deep experience in modern ML technologies and platforms, I love helping teams develop and execute AI strategies, leverage modern ML, and scale and speed up use of ML across products. I have led several teams through design, development, deployment and operations of services at scale. I have strong experience leading geo-distributed teams, coaching and mentoring leaders (technical, management), collaborating across functional boundaries, and working with a variety of senior executives. In my second stint at Adobe, I lead Generative AI initiatives to transform how we consume, comprehend and create content in the AI-era. At Persefoni, I led an amazing team to build technologies that will help save planet earth! At Adobe I helped apply cutting edge AI to reimagine how we experience documents. As a Senior Director of Machine Learning at Workday, I helped scale and speedup the use of ML across Workday product teams. I led teams of ML and data engineers that are passionate about Enterprise AI. I believe in ethical AI development and regularly partnered with Privacy, Legal and Security experts to codify complex compliance rules into concrete software services. Previous to joining Workday, I was a Director at Symantec and led a team that helped build a real-time streaming analytics service. I joined the team as the leader with just one engineer and then quickly grew the team and delivered the first version of the streaming analytics service in 3 months! In the past, I was a researcher at IBM T.J. Watson research center, where I built cloud anomaly detectors and ML driven compiler optimizations. I (co)authored a lot of publications during my time at IBM Research! I have a Ph. D. in computer science with 40+ publications/patent
Colorado State University
PhD, Computer Science
January 1, 2001 – January 1, 2008
Indian Institute of Science (IISc)
M.Sc. (Engr), Computer Science
January 1, 1996 – January 1, 1999
Madurai Kamaraj University
M.Sc, Computer Science
January 1, 1994 – January 1, 1996
University of Madras
B.Sc., Mathematics
January 1, 1991 – January 1, 1994
Adobe
VP, Generative AI
December 1, 2025 – Present
Adobe
Sr. Director and Head of AI / ML Engineering, Document Cloud
March 1, 2023 – November 1, 2025
Persefoni
VP of Machine Learning & Engineering
March 1, 2022 – March 1, 2023
Newark, California, United States · Remote
Adobe
Sr. Director and Head of AI / ML Engineering, Document Cloud
May 1, 2020 – March 1, 2022
San Jose, California, United States
Workday
Senior Director, Machine Learning
May 1, 2019 – April 1, 2020
San Francisco Bay Area
Workday
Head of Data Science and ML Architecture, Director of Engineering
October 1, 2018 – April 1, 2019
San Francisco Bay Area
Workday
Head of ML Architecture, Director of Engineering
February 1, 2018 – September 1, 2018
San Francisco Bay Area
Workday
Director of Engineering, ML Platform and Search
April 1, 2016 – January 1, 2018
San Francisco Bay Area
Symantec
Technical Director, Cloud Platform Engineering
May 1, 2014 – March 1, 2016
Mountain View
IBM TJ Watson Research Center
Research Staff Member
August 1, 2007 – April 1, 2014
Yorktown Heights, New York
Hewlett-Packard Laboratories
Summer Research Intern
May 1, 2002 – August 1, 2002
Palo Alto, California
IRISA/INRIA
Research Assistant
January 1, 2000 – May 1, 2001
Rennes Area, France
HP
Senior Software Engineer
January 1, 1999 – December 1, 1999
Bengaluru Area, India
Volta: real-time streaming analytics service
March 1, 2016 – Present
I led the Data Services team to build a real time data analytics service called Volta. In my role as an architect I led the team through design, development, deployment and operations of the service. ◆ Volta is a realtime data analytics pipeline coupled with cloud services built on Kafka, Storm, ElasticSearch and InfluxDB. ◆ Volta is used in production at Symantec for collection, storage and query of log, metric and other kinds of event data. ◆ Volta ingests and processes billions of events per day in real-time.
Zookeeper based software coordination framework for OpenStack Heat
May 1, 2013 – April 1, 2014
Designed and built a high-level software deployment coordination framework for OpenStack Heat. The framework provides a high level model for users to specify the software stack that needs to be configured and the dependencies between them. Dependence analysis and template based code generation is used to automatically generate a Heat template that realizes the coordination via Zookeeper. I was invited to present its use for Enterprise Applications in OpenStack Summit 2013 you can see the recorded video here: http://www.youtube.com/watch?v=QGPP7y5Vj0U
Cloud application fingerprinting
March 1, 2013 – April 1, 2014
Configuration and deployment bugs are major problems for applications deployed in the cloud and these types of bugs are hard to diagnose. I designed and built an application-fingerprinting scheme that captures all the services, packages and network information related to an application. This application fingerprint has formed the basis for diagnosis and trouble-shooting tools.
Approximate computing via programming with relaxed synchronization
May 1, 2012 – April 1, 2014
This work explores the promise and potential of approximate computing. We have developed a paradigm for parallel programming with relaxed synchronization and have demonstrated, on analytics and machine learning benchmarks, that large (up to 10x) performance improvements can be gained without sacrificing quality of results. This work has been published and presented in several venues.
Configuration Anomaly Detection in Cloud
May 1, 2012 – April 1, 2014
We have developed a novel scheme for detecting anomalies in configurations of applications deployed in cloud. Our scheme is able to detect several real world misconfigurations in Apache, MySQL, and PHP applications on two clouds – public cloud (AWS) and private cloud (IBM). Our scheme has been published in a top systems conference (ASPLOS’14)
Selection and management of resource configurations and software bundles for Virtual Machines in cloud
May 1, 2012 – April 1, 2014
In this work we explored techniques to be used by a cloud provider for supporting large enterprise customers. We developed -- a framework, named CloudAffinity, aimed at selecting an optimal number of resource configurations (CPU, memory, and disk) based on customer (workload) requirements. -- an algorithm for selecting software bundles for virtual machine images and examines the impact of bundle selection on the number and characteristics of resulting images.
Automatic Parallelization using the Polyhedral Framework
August 1, 2007 – August 1, 2012
In this work we built an Automatic Parallelization and Loop Optimization Framework for the IBM XL C/C++/FORTRAN compiler. One of my contributions is a loop tiling technique that significantly improves register locality and instruction level parallelism leading to single thread performance speedup of 2x - 4.5x.
Jeff Weiner on Managing Compassionately
June 24, 2026 – Present
Agile Technical Product Owner
Scrum Alliance
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career trajectory shows a strong inclination towards innovation, research, and leading cutting-edge technology initiatives, particularly in AI/ML and large-scale systems. Their academic background (PhD, multiple Master's degrees) and numerous research projects highlight a continuous learning mindset and a drive for solving complex problems. The diversity of projects, from real-time analytics to compiler optimization and cloud configuration, indicates adaptability and a broad technical interest. However, the recent roles are heavily focused on AI/ML leadership, which, while valuable, might require a re-evaluation of their hands-on backend engineering skills for a pure 'Backend Engineer' role, potentially indicating a slight mismatch if the role is not heavily AI/ML focused. The long tenure in leadership positions might also suggest a preference for strategic over direct implementation work.
Soft Skills & Operational Fit
The candidate's extensive leadership roles (VP, Sr. Director, Director) across multiple companies demonstrate strong leadership, strategic thinking, and team management skills. Their involvement in teaching ML courses and evangelizing ML dev strategies indicates a proactive approach to knowledge sharing and mentorship. The descriptions of leading teams through design, development, deployment, and operations suggest a strong operational fit and ability to manage complex projects end-to-end. The focus on ethical AI development also points to a responsible and thoughtful approach to technology.