Apr 2024 – Present · 1 yr 11 mos · San Francisco Bay Area

• As VP of Engineering at Apollo, I lead Machine Learning, Data, and several product verticals that power the world’s fastest-growing GTM platform. I oversee a global organization of ~100 engineers and builders across APAC, North America, and Europe.
• I’m driving Apollo’s transition into an AI-native company, reimagining how we build, operate, and scale products. Through broad adoption of AI tooling (for coding, code reviews, incident management, testing, etc.) and workflows, we’ve transformed engineering productivity and accelerated innovation across the org.
• I also lead the development of Apollo’s AI Assistant, one of the most advanced AI agent platforms in the GTM space. With agents for Prospecting, Sequences, Workflows, Analytics, and Enrichment already live, and next-generation capabilities like Agentic Workflows and Agent Builder underway, we are redefining how modern teams go to market.
• At the intersection of AI, product, and organizational scale, my focus is on shaping Apollo into not just a category leader, but a pioneer of the AI-agentic era for GTM.

Jan 2022 – Apr 2024 · 2 yrs 3 mos · San Francisco Bay Area

• Apollo.io is a leading provider of sales intelligence products. Backed by Sequoia Capital and Bain Capital Ventures, it became the first unicorn in the sales domain in 2023 with a valuation of $1.6B. I lead all things data and machine learning at Apollo, including all full-stack verticals that utilize data and ML. My org has 10+ teams led by managers, senior managers, staff engineers, and principal engineers, with a total size of 75+ people. These teams are spread across 5+ countries and operate in a fully remote setting.
• My org’s horizontal charter includes Data Platform, Data Engineering, ML Platform (including LLM Ops), ML Engineering, and Search Relevance. My vertical charter includes Apollo’s foundational products like Data offerings, Prospecting (search engine), Sales Engagement, and Plays.
• I am also leading Apollo’s Engineering Hiring program and have successfully hired more than 150 engineers on the Engineering team, a growth of ~700% since Jan 2022.

Nov 2020 – Oct 2021 · 11 mos · Menlo Park, California, United States

• Storepath helped eCommerce sellers in expanding their business to multiple channels and countries. Our AI and data-driven approach allowed us to automate most aspects of the eCommerce business so sellers, brands, and merchants can focus on building and improving their products.
• Storepath raised $4M in funding from Fika Ventures, Neo (Ali Partovi's VC firm), Act One Ventures, and other angels.
• I led the technology (and whatever else that came my way) at Storepath. We launched approximately one big product per month and experimented rapidly to gather quick feedback from customers. Some of our notable products were: AI-powered seller tools (Copy Creator, Keyword Researcher, Listing Optimizer, Merchant Portal), an on-demand marketplace (webapp and mobile app) to connect eCommerce brands with freelancers, a Shopify app for automated Copy Creation using OpenAI's GPT-3 models, and an ML model to predict the valuation of eCommerce businesses.

Jan 2020 – Dec 2020 · 11 mos

• Led multiple full-stack teams and initiatives, launching multiple highly impactful projects for student Admissions and Learning.
• Introduced and set up various engineering processes like Scrum, Oncall, and Engineering Onboarding while improved many others like Hiring, Roadmap, Tech Planning, and Engineering Documentation.
• Scaled up engineering teams and established good cross-functional working relationships that increased the overall productivity of the company. Handled build vs buy decisions for various products.
• Led initiatives responsible for 200% growth in student enrollments and a 50% drop in student withdrawals.
• Launched the Growth Experimentation program responsible for 15% increase in enrollment conversions.

Oct 2019 – Jan 2020 · 3 mos

Oct 2018 – Oct 2019 · 1 yr

• I led the Data & Machine Learning Platform team at Amazon Music. My team built tools and platforms to help Scientists and Engineers access, process, compute, experiment, store & share data and build scheduled machine learning and other complex data workflows in production environments. Our platforms ran at Amazon scale processing billions of events per day.
• My team was closely aligned with Machine Learning, BI, and Product teams and we contributed to multiple cross-team programs. We owned the end-to-end batch data infrastructure at Amazon Music which includes the behavioral data ingestion ETLs, Redshift based data warehouse, Tableau based reporting, and Spark based data processing workflows. We also built platforms for large scale machine learning experimentation, workflow orchestration, and data accessibility. Being the custodians of Amazon Music customers' data, we were responsible for building security and privacy features in our platforms. Our products had high adoption rate and powered crucial features on the Amazon Music app, including personalization and music discovery.

Oct 2016 – Oct 2018 · 2 yrs

• I started the Data Science Platform at Amazon Music. Strategized the ML Platforms at Amazon Music, aligning closely with the central Amazon and AWS ML Platform teams. Worked with AWS Glue and AWS SageMaker teams during the early days of these products and built similar platforms at Amazon Music.

Apr 2015 – Oct 2016 · 1 yr 6 mos · Palo Alto, CA

• Led the development of various search relevance and query understanding initiatives, including, category correction, product type identification in search queries, and building out the backend data infrastructure. These products were launched to millions of users and handled millions of search queries per day.

Feb 2013 – Apr 2015 · 2 yrs 2 mos · Palo Alto, CA

• Built features and integrations in Amazon's Ad Exchange, primarily serving contextual ads. The service processed billions of requests per day. Worked under tight SLA, throughput, and latency requirements.

Jan 2011 – Feb 2013 · 2 yrs 1 mo · San Francisco Bay Area

• Work included Named Entity Recognition, crowd-sourced data labeling using Amazon Mechanical Turk, Sentiment Analysis, Ad-targeting, classification, topic extraction, tagging, ETL processing, unstructured text annotator pipelines, and hadoop based systems. Contributed to open source (UIMA, ClearTK, Chukwa), including up-gradation of Concept Mapper UIMA annotator to version 2.4.

Jun 2010 – Aug 2010 · 2 mos

• Implemented the boost-Clustering algorithm using K-means as the base clustering algorithm, on the GameSpot (www.gamespot.com) data of CBS. Applied topic modeling using LDA (from Mahout library) to reduce the dimensionality of the input documents and gave this as input to the Boost-cluster algorithm.

Aug 2010 – Dec 2010 · 4 mos

• Teaching assistant for "Java and J2EE Programming".

Aug 2009 – Dec 2010 · 1 yr 4 mos

• Pursued Masters in Computational Data Science (formerly, VLIS).

Dec 2008 – Feb 2009 · 2 mos

• Built an Open Source generic Recommendation System in Ruby called Prastava based on a hybrid algorithm of content based filtering and collaborative filtering.
• Project Guide - Dr. Min-Yen Kan, Assistant Professor, NUS
• Team Size - 2 (Himanshu Gahlot and Tarun Kumar)
• Web Service Link - www.aye.comp.nus.edu.sg/~himanshu/pr/
• Project hosted at Github - https://github.com/hgahlot/Prastava

May 2008 – Sep 2008 · 4 mos

• Worked as an intern on two projects : "Named Entity Recognition for Hindi" and "Shallow Parsing for Hindi". These projects were intended to be part of the automatic annotation system of the Hindi language processing tool "Sanchay". Obtained state of the art results in both tasks.
• Team Size - 2 (Himanshu Gahlot and Awaghad Ashish Krishnarao)
• Project Guide - Anil Kumar Singh, Research Scholar, IIIT Hyderabad