At Default, my role is to make sure that we help startups build high-quality products, in time. During my tenure at Default ‣ We have worked with 30+ clients funded by marquee investors such as Sequoia, Lightspeed, Elevation Capital, Kaalari Capital, Omidiyar Network etc. ‣ Worked with Unicorn startups such as Zenoti, OyoRooms, Paisabazaar, Cure.fit ‣ Helped 10+ founding teams launch their MVP ‣ Have our apps featured in the Google playstore. ‣ Shipped 20+ products across Fintech, AgriTech, consumer internet, and SaaS. ‣ Helped startups hire and onboard developers in a matter of days (repeatedly).

Level AI is a San Francisco based AI/Data Science Company. I was the founding member of the engineering team, tasked with building product architecture, backend services and building a team. At my time in the company I worked on two projects. The first product was a social networking platform specifically for financial news and decision making. The product was available on android, iOS and web. For this product: a. Hired the engineering team for data science, mobile and web. b. Built the backend infrastructure using python and flask. c. Built real time features such as push notifications (for both web and mobile) using Kafka and redis. d. Set the software architecture of the backend flask app through extensive use of flask middleware hooks and a custom flask-router. e. Built features such as network graph, topic recommendation, follow recommendation etc. Technologies used: Python, Flask, MongoDB, Google Cloud, ReactJS, Redux, Android, iOSThe second product at level (currently live) is a voice enabled assistant to automate workflows for enterprise workers. The product has various use cases from logging events to customer service. A comparable product in the market is google dialogflow. a. Built a NLP system to identify intents and entities using RASA NLU b. Built a data pipeline to generate synthetic data and train NLP models. c. Built a search system for fulfillment layer, post intent and entity classification d. Drove the product for internal data annotation tool e. Developed a self serving system to configure and generate models for any enterprise/data-setTechnologies used: Python, Django, PostgreSQL, AWS, GCP, RASA NLU, Tensorflow

Acadview was an EdTech platform for skilling engineering students on programming for various platforms such as python, web development, machine learning and android. We conducted online and offline lectures to skill over 5000 students across North India. The startup was acquired by Upgrad (a leading edTech company in India in 2018). My role at acadview was that of a full stack entrepreneur:1. Built a high performant engineering team from scratch on a monthly budget of $3000, I achieved this by leveraging good folks who needed just the right amount of mentorship on engineering and technology. 2. Built a data ingestion framework which could handle excel sheets of different formats and inconsistencies (we were getting semi-structured data from universities). Built a PDF generation pipeline which could scale to millions of PDFs.3. Built the first few version of the LMS (both frontend and backend) that was used to conduct classes, schedule assignments, conduct project evaluations. 4. Lead the operations team in both a strategic and executive capacity to select high quality instructors and support. 5. Led the content effort to generate relevant content which was in tune with our target audience by using lay-person language, feynman technique, memes.6. Led the product effort to product high quality PRDs for the engineering team.

I was hired at frankly as a backend engineer (python + flask). I was amongst the first member of the core team and my role quickly became horizontal where I contributed in multiple ways. Frankly was a video Q & A platform for celebrities and influencers. One could ask a question to a celebrity and get a video response. In a short time span we on-boarded esteemed personalities from politics, bollywood and sports. In terms of my personal contribution: a. Built the search system using ElasticSearch, the search was done on usernames, query phrases, video meta data and personalized based on user preferences and geography. b. Led the Android product team in both product and technical capacity. c. Set up weekly release cycles for the android team doing weekly updates to the app. d. Picked up a bit of android development as well, built a couple of small features and bug fixes on the app. e. Led the hiring effort for the engineering team with a team of 10+ recruiters, internal and external

Quizot was a content platform meant for quizzers and trivia lovers. We differentiated ourselves by carrying highly curated content and targeting a niche category early on. To seed the platform with content I launched a virtual internship program. At it’s peak the platform had ~23,000 MAU with 800,000 pieces of content consumed every month. We were bootstrapped and spent a total of $1500 over a period of two years.

Started my career as a QA (manual testing)