Executive Summary

Stories from Work

Spherex, Inc - Los Angeles / Santa Clara, California (2019 - Current)

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As a founding team member at Spherex and the Head of Product Management for AI Products, reporting into the CEO/co-founder, I oversee the end-to-end strategy for our multi-modal AI platform, which culturally indexes both short-form and long-form video content. The platform analyzes video in three stages: detecting (or flagging) objectionable events, interpreting these events based on cultural and territorial policies, and classifying them across various use cases—including age suitability, compliance, brand safety, and ad relevance.​ Recent innovations in video streaming enable instant global releases—meaning one title can be distributed in over 200 countries simultaneously. However, this wasn’t always feasible due to language localization and the need to comply with each region’s policies and regulations. Recognizing this opportunity, we envisioned a mindset of a machine-first (AI-first) approach—while keeping humans in the loop to scale content operations efficiently; while the user experience followed human-centered design principles.

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Leading Product Management in AI in constantly changing environment

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My role involves defining problems, applying design thinking to build the product/platform, collaborating with UX researchers on User flows, developing prompts as part of the algorithms and data pipelines, refining taxonomies, and working closely with Engineering to deliver solutions. Concurrently, I led the roadmap of developing foundational models in a multi-modal (audio, vision/images, text) setup, creating pipelines to generate and interpret events. One of the perks of a Silicon Valley startup is the chance to be hands-on throughout product creation: in the past two years in addition to the foundational models, I’ve prompt engineered a pipeline for detection to classification by deploying large language models locally, fine-tuned them using in-house training and testing datasets, and adopted new AI advancements from the ground up.

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The job came with several challenges, such as (a) justifying huge R&D budgets, (b) motivating engineering teams to balance commercial goals with exploratory work, (c) continually evolving the architecture to leverage new technology, (d) embracing pivots as technology advanced, and (e) differentiating our offerings through benefits innovation, coverage, and competitive pricing.

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From 0 to 1 and now 1 to 100​

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Highlights of this journey include onboarding leading film and television content creators, launching the industry’s first multi-modal video classification AI product, growing from 0 to 1 in the early stages, and now scaling from 1 to 100 with Product-Led Growth (PLG) strategies in the product’s second generation. I also jointly filed 9 patents of which 7 patent got approved/granted by the USPTO. Additionally, I published several white papers in M&E journals.

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Skills Gained:

1. Hands-on AI Practitioner - 
   Beyond prompt engineering, I’ve prototyped and fine-tuned models with tailored training and testing data. I also evaluate diverse large language models such as open-source Deepseek R1, Llama, closed models like Claude Sonnet 3.5, OpenAI 4.0.1, and more.

2. User-Centric AI Integration - 
   I learnt to translate AI insights into seamless user experiences by working backward from the customer’s needs—often resulting in both visible and behind-the-scenes innovations that drive product value.

3. Product Leadership - Driving change through uncertain times with leading initiatives and influencing cultural shifts through constant innovation and learning.

V2Solutions, Los Angeles/Santa Clara, California (2014 to 2019)

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During my MBA at Pepperdine University, I had received an offer ​from my Strategy class Professor (ex-CEO of Patagonia) to help researchers from UC Santa Barbara launch a startup with a patented glass-making technology. Although I felt honored and got excited, I didn't feel that I was ready for the uncertainties and the big financial risk that came with the startup. I always wondered if I would get another such opportunity - so that decision was a constant regret. While at Disney, another Professor in Supply Chain reached out and suggested I talk to this firm, V2Solutions (startup from Silicon Valley). I jumped on this opportunity as the idea to lead Product Management in the Data Science and Analytics appealed to me and I didn't want to regret losing it - plus this was out of my comfort zone and I wanted to give it a try. When I spoke with the founder and the SVP of the media division, it became clear that the people behind the idea were good to work with and that it was going to be an interesting ride.

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Content Monitoring & Analytics Platform in the Cloud

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At V2Solutions, I helped create a cloud-based Metadata and Content Monitoring platform in Analytics and Data Science (ML) for Media and Entertainment industry. From my previous experience at Sony, Warner Bros., and Disney, I had an understanding of the inefficiencies in the M&E supply chain. Furthermore, talking to leaders at Paramount, NBCUniversal, Lionsgate studios, I was able to draft a solution proposal for my founders to look into - there was an opportunity nobody was willing to get into. Data is usually messy and is a headache to manage if it is not a part of your core competency. I helped define a solution that allowed web-crawlers to collect, normalize, and analyze media-related data from global retailers and the visualize it as part of a platform to provide strategic insights that the media companies could act upon - like Pricing, Availability, Performance, Advertising readiness, etc.

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Highlights of working at V2Solutions were the detailed user journeys that I was able to create with the help of customer 1:1s, launch the product at MIPCOM, France, get global feedback on the positives and negatives of the product, onboard most of the major studios, create a positive cash flow for the company, and most importantly get confident about working at startups.

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Skills Gained:

1. User Research and understanding Customers through interviews for jobs-to-be-done

2. Product Design and Management 

3. Data Governance

4. Machine Learning and Knowledge Graphs