Summary
Overview
Work History
Education
Skills
Timeline
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GAURAV GARG

Bengaluru

Summary

Staff Machine Learning Engineer with proven expertise in leading the full ML lifecycle—from data preparation and modeling to deployment and monitoring—across high-impact AI products. Experienced in building Copilots, recommendation engines, and large-scale ML systems adopted by thousands of enterprise customers worldwide. Recognized for driving clarity in ambiguity, mentoring teams, and transforming complex challenges into scalable, user-centric solutions that accelerate adoption and deliver measurable business value.

Overview

7
7
years of professional experience

Work History

Staff Machine Learning Engineer

Zscaler
03.2022 - Current
  • Built and deployed multiple LLM-based systems (ZDX Copilot, Code Copilot) using both open-source (Phind CodeLlama, custom bi-encoders) and closed-source LLMs (OpenAI GPT-4, Google Gemini 2.5 Pro). Applied LLMs for entity extraction, intent classification, code generation, data analysis, and Q&A; in enterprise-scale environments.
  • Led development of ZDX Copilot, a GA product now serving 3,000+ enterprise customers globally. Designed and shipped multiple components including Q&A; & Data Analysis Agent, Next-Prompt Suggestion System, Memory Management Module. Built on NVIDIA NeMo Guardrails, ensuring safe and compliant outputs.
  • Led modeling for Policy Recommendation Engine, ensuring precise recommendations through novel clustering and segmentation techniques. Introduced co-occurrence–based segmentation, improving acceptance rates from 25% → 62% and resulting in a published patent. Increased customer adoption and drove product stickiness.
  • Ideated and drove Data Center Alerting System, monitoring 100+ data centers for potential breaches while minimizing false positives. Delivered PoC in 2 weeks, secured stakeholder buy-in, and led a 4-member team to production launch in 2 months. Became an upsell revenue stream.
  • Built Code Copilot Tool leveraging RAG architecture with vector embeddings and open-source Phind CodeLlama for context-aware code generation. Implemented code component extraction and evaluated outputs using RAGAS, ensuring alignment with developer workflows.
  • Conducted multiple customer interactions with design partners to validate requirements, gather feedback, and iteratively refine recommendation and Copilot outputs, ensuring customer-driven development.
  • Optimized ZDX Copilot latency by designing an asynchronous job-management utility. Coordinated across component owners, enabling parallelized execution and achieving 4s average latency reduction.
  • Mentored and guided new team members, providing technical direction, reviewing designs, and unblocking tasks. Enabled a new hire to independently own ranking improvements under tight timelines.
  • Partnered with PMs, UI, infra, and platform teams to align on requirements, prioritize deliverables, and ensure scalable deployment of ML/LLM systems across enterprise-scale datasets.

Associate Data Scientist

JP Morgan Chase & Co.
07.2019 - 03.2022
  • Developed an insightful retirement prediction model leveraging customer demographics and transaction data, utilizing PySpark, Hadoop, and SQL to enhance forecasting accuracy and provide actionable insights.
  • Delivered actionable insights to LOB leaders, resulting in a 15% increase in operational efficiency and enhancing decision-making processes.
  • Built data pipeline to store customer life events insights using Airflow, used by other data analytics teams.
  • Celebrated in 2020 Q2 Recognition Scroll for outstanding contributions as nominated by Team Lead, inspiring team motivation and collaboration.
  • Successfully transitioned 3 applications to password-less integrations with Kerberos and CyberArk, enhancing security protocols and streamlining role-based access management.
  • Utilized Kubernetes and Docker to streamline deployment and containerization for 10+ applications, following CI/CD principles to boost deployment speed by 30% and minimize downtime.

Data Science Intern

Home First Finance Company
05.2018 - 07.2018
  • Built a property price prediction tool from scratch for Tier I, II, and III cities of India using R.
  • Collected and cleaned data from 1.1M properties through web scraping, utilizing advanced data cleaning techniques and exploratory data analysis to uncover market trends and insights.
  • Utilized Extreme Gradient Boosting to develop a high-accuracy predictor model, enhancing forecasting by 32% and driving data-informed decision-making for the team
  • Developed an interactive UI dashboard using the Shiny library, integrating a dynamic map and intuitive search bar to enhance data accessibility and user engagement.

Education

Bachelor of Technology - Chemical Engineering

IIT BHU Varanasi
05.2019

Master of Science (part-time) - Data Science and Machine Learning

Liverpool John Moores University
03.2022

Skills

  • Programming languages: Python, C/C, Java, SQL
  • Machine Learning & AI: TensorFlow, PyTorch, LangChain, Hugging Face, vLLM, RAG, VectorDB, NeMo Guardrails
  • MLOps & Deployment: Docker, Kubernetes, Git, CI/CD, FastAPI, Flask
  • Data & Cloud Platforms: BigQuery, PostgreSQL, AWS, GCP
  • Leadership & Collaboration: Leading small teams, Mentorship, Stakeholder Alignment, Driving Roadmaps, Cross-functional Communication

Timeline

Staff Machine Learning Engineer

Zscaler
03.2022 - Current

Associate Data Scientist

JP Morgan Chase & Co.
07.2019 - 03.2022

Data Science Intern

Home First Finance Company
05.2018 - 07.2018

Bachelor of Technology - Chemical Engineering

IIT BHU Varanasi

Master of Science (part-time) - Data Science and Machine Learning

Liverpool John Moores University

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GAURAV GARG