Manoj Shridhar Hegde

About Manoj Shridhar Hegde

I’m a Senior Software Engineer at Intuit specializing in fraud prevention, identity, and device intelligence systems at scale. Over the past few years, I’ve led the design and delivery of security and risk platforms that power TurboTax and other Intuit ecosystems, protecting tens of millions of customers from fraud while enabling seamless user experiences.My work spans backend systems, real-time decision engines, and large-scale data platforms. I’ve modernized legacy data pipelines to Databricks (processing ~6M records/day), reduced infrastructure costs by ~$200K/month through platform optimization, and built reusable proxy-caching architecture for device intelligence services that is now positioned as a company-wide standard. I’ve also driven fraud model improvements (8% precision lift, 12% recall lift), enabled CK2TT seamless authentication for 55M+ users, and contributed to deletion of 15.7M stale accounts to unlock 0-click login experiences.Beyond delivery, I focus on high-quality system design and scalable architecture — from vendor-agnostic proxy services (NGINX, Lua, Redis, Docker) to policy engine modernization and real-time risk signal integration. I thrive in ambiguous, high-stakes environments (especially during tax peak) and am known for bias for action, deep technical ownership, and cross-functional collaboration across engineering, policy, and identity teams.Recently, I’ve been expanding into AI-native fraud detection and GenAI applications, working on vector-based session fraud detection, in-session ATO risk modeling, and applied AI experimentation through Intuit’s accelerator programs.I’m passionate about building secure, intelligent systems that balance customer trust, scale, and speed - and I aspire to continue growing into broader technical leadership roles in identity, fraud, and AI-driven risk platforms.

Experience

1. Senior Software Engineer

   Intuit

   Jun 2024 — Present · Bengaluru, IN

   Driving scalable fraud detection and platform modernization at Intuit, delivering high-precision ML systems, ecosystem-level ATO defense, and cost-efficient device intelligence architecture. Fraud prevention and Device Intelligence engineer driving large-scale identity and risk systems at Intuit. Led architecture and deployment of reusable vendor proxy infrastructure, enhanced real-time device trust frameworks, and delivered measurable fraud precision (+8%) and recall (+12%) improvements. Passionate about AI-native risk detection, scalable backend systems, and building secure, seamless customer experiences.

Education

• JAIN College

  12th grade, PCME

  2012 — 2014

• PES University

  Bachelor's degree, Computer Science

  2014 — 2018

• University of Southern California

  Master's degree, Computer Science

  2019