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CEO, Pomo | Book a demo @ usepomo.ai | ex- Google DeepMind
(If you’re a recruiter, appreciate the interest, but I'm working on something new.) I’m building at the intersection of AI, products, and real-world impact. After 10+ years in AI research and engineering, leading Applied GenAI efforts at Google DeepMind (Imagen, Gemini Nano Bananas). I’m now focused on translating cutting-edge AI into products that actually move markets and improve lives. My experience spans from scaling frontier models to deploying systems across ads, recommender systems, and industrial control applications. I’ve led cross-functional teams, owned 0→1 initiatives, and driven products from research to production at global scale. Today, I’m focused on building and scaling AI-driven products that deliver measurable value, not just technical novelty. I care deeply about speed, distribution, and creating systems that compound over time. Beyond building, I invest in people and ecosystems, mentoring founders, advising AI communities, and contributing to initiatives that make AI more accessible and impactful.
Stanford University Graduate School of Business
Executive Education: Business Administration, Entrepreneurship/Entrepreneurial Studies
N/A – Present
Carnegie Mellon University
Master’s Degree, Computer Engineering, Machine Learning
N/A – Present
Vellore Institute of Technology
Bachelor’s Degree, Electronics and Communications Engineering
N/A – Present
Pomo
CEO, Co-Founder
September 1, 2025 – Present
San Francisco, California, United States · On-site
CMU Tech & Entrepreneurship
Advisor
May 1, 2021 – July 1, 2025
Google DeepMind
Research Engineer
January 1, 2020 – August 1, 2025
San Francisco Bay Area · On-site
Machine Learning Engineer
February 1, 2018 – December 1, 2019
San Francisco Bay Area
Unity Technologies
Machine Learning Engineer
January 1, 2017 – January 1, 2017
San Francisco, California
Machine Learning Dept. at Carnegie Mellon University
Teaching Assistant
January 1, 2016 – January 1, 2017
Pittsburgh, Pennsylvania
Jio
Software Engineer
January 1, 2016 – January 1, 2016
Mumbai Metropolitan Region
Teamonk
Co-Founder
April 1, 2015 – November 1, 2016
Bengaluru, Karnataka, India
Pravega Racing
Electronics Engineer
November 1, 2012 – August 1, 2014
Vellore,India
FaceGAN(PennApps Spring'17)
January 1, 2017 – Present
Generative Adversial Networks(GAN's for short) has been one of the most exciting fields in the Area of Machine Learning in the last 10 years.It generates new artificial data mimicking a real data distribution.The Results have been impressive producing samples which cannot be differentiated from the "Original Data". Hence,we can take advantage of this property to Reconstruct Media from partial/noisy samples. This was created at the PennApps Spring'17 Hackathon and finished as the Top 30 Finalist among 178 teams.
Face Expression Recognition Using Dense and Convolutional Neural Networks
November 1, 2016 – December 1, 2016
Developed a CNN Architecture using Theano for 7 Class Expression Classification:Anger,Disgust,Fear,Happiness,Sadness and Surprise.This was predicted on 4,000 Images on the FER2013 Data set achieving 59.75% Accuracy. A Dense 10 -Layer Neural Network was also trained on the Cohn Kanade Data Set.The Viola Jones algorithm and LBF Histogram(For Eyes,Nose and Mouth) were utilised for this.
Trends in Technologica Advancements -Practical Data Science Project
October 1, 2016 – December 1, 2016
Academic papers, patents and trademarks are often a very good indication of the direction in which a field of research is evolving. In this project, we aim to procure and analyze millions of patents, published academic work and trademarks. The procured documents must range over a wide period of time (~20 years) and over diverse fields in order give us a sense of how academic research has evolved over time and how inter and cross disciplinary research has grown. Project Accomplishments: Identify key areas of research in specific years by building a bag of words (TF/TFIDF) model. We pick up scientific key words and map them to areas of research. Identify key trends in the development of specific fields of research (eg. Advances in Deep Learning). We select a single field of research and visualize publication trends specific to that field over the years. Develop a Latent Drichlet Allocation model for the entirety of the absract data and identify 100 distinct topics and coresponding key words. This is an unsupervised approach to identifying fields of research from the data. As a fun experiment we generate a new abstract from the N-gram model that we develop from the entire abstract data.
Dr Jarvis(PennApps Hackathon 2016)
August 1, 2016 – Present
Utilised the Amazon Echo hardware for creating a Healthcare AI.The Backend in Python was integrated with MongoDB for the Database and Twilio API.The User queries were passed onto Amazon Web Services(lamda for Echo) and extracted processed responses from the Backend. Utilised Natural Language Processing for Understanding User Queries and Computer Vision for Image Classification.
Electronics Engineer-Formula Student
November 1, 2012 – July 1, 2014
Worked for my Universities Formula Student Team as an Electronics Engineer.This involved working on the general wiring harness,launch and traction control systems and all other hardware components. ,Was engaged in the Cost Report(detailed summary of the cost involved in the manufacturing of the vehicle) and Business plan presentation(to pitch our automotive start up to venture capitalists) aspects as well.
Private Aircraft Pilot
Federal Aviation Administration
June 24, 2026 – Present
Learning How to Learn: Powerful mental tools to help you master tough subjects
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background shows a strong inclination towards cutting-edge AI research and entrepreneurship. Their project diversity includes hackathons, academic research, and even an e-commerce startup, indicating adaptability and a broad range of interests. However, the target role of 'Data Analyst' is a notable departure from their senior-level AI/ML engineering and research roles. While their analytical skills are undoubtedly strong, their career trajectory and stated interests might suggest a preference for more advanced, research-heavy, or leadership-oriented roles within data science or machine learning, rather than a traditional data analyst position. This could lead to a potential mismatch in long-term career aspirations and day-to-day role expectations.
Soft Skills & Operational Fit
The candidate's experience as a Co-Founder and Advisor, along with participation in hackathons, suggests strong initiative, problem-solving, and leadership skills. Their involvement in Formula Student teams indicates teamwork and practical application of engineering principles. The descriptions of their roles at Google DeepMind and Google highlight collaboration and the ability to drive projects from research to deployment. However, the target role is 'Data Analyst', which is a significant shift from their primary experience in advanced AI/ML research and engineering. While they possess strong analytical capabilities, their operational fit for a pure Data Analyst role, which often involves more routine data manipulation, reporting, and business intelligence, might require adjustment.