Makarand Gujarathi

Makarand Gujarathi is an enterprise software engineer and platform architect whose two-decade career has been defined by a consistent pattern: taking systems that operate at scale—often under operational stress, regulatory constraint, or peak retail demand—and rebuilding them into platforms that are faster, safer, more observable, and measurably more efficient. Across Fortune 500 environments including Walmart, Fannie Mae, Barclays, and Genworth Financial, he has delivered modernization programs that do not merely ship features, but change the operating physics of critical systems—reducing outages, compressing cycle times, and creating reusable technical standards that outlast any single project.

At Walmart, Gujarathi has worked on technology that directly touches day-to-day operations across the retail footprint. As a Senior Software Engineer, he led the architecture and full-stack development of a real-time self-checkout monitoring mobile application (React Native) used by 100,000+ daily active users across 4,500 stores. The platform processes 12–15 million alert events and 20+ million transaction events across the enterprise. He designed a scalable, multi-channel WebSocket communication architecture for real-time signaling, modernized client state management from Redux to Zustand to eliminate UI latency, and implemented observability pipelines using Firebase and Google BigQuery to bring production behavior into measurable focus. The result was operationally tangible: associate response times improved by 15–20%, and adoption rose 10–15% compared to the legacy tooling—directly improving store-level efficiency where minutes matter.

More recently, he architected and delivered an AI-powered production support automation system leveraging Large Language Models and Model Context Protocol to automate incident analysis and root-cause identification. Rather than treating AI as a novelty layer, this system was designed to reclaim engineering time and stabilize operations—recovering ~20% of engineering capacity previously spent on manual investigations, reducing Mean Time to Resolution, and establishing a reusable automation framework with potential to scale across many services.

His work at Walmart also includes high-tempo platform rescue and cost discipline under production constraints. He led a rapid decomposition of the Upfront application from a monolithic backend into four independent microservices in one month, restoring stability and eliminating recurring outages. He then led the migration to Walmart’s Cloud Native Platform (Kubernetes), including intelligent auto-scaling strategies that handled baseline demand through peak seasonal surges—supporting 60–80K daily users with 99.99% availability while delivering an estimated ~60% cumulative reduction in infrastructure costs. On the data layer, he drove modernization from SQL Server to Azure Cosmos DB using a dual-database, parallel-run architecture with instant rollback, designed active-active multi-region configurations, and created a reusable Cosmos DB access library that became a reference implementation—improving resilience and removing single points of failure.

Before Walmart, Gujarathi’s impact is equally visible in regulated financial ecosystems where correctness, security, and auditability are non-negotiable. At Fannie Mae, he contributed to the Data Validation Service (DVS) 2.0 powering Desktop Underwriter and the Day 1 Certainty program. He implemented Spring Boot REST services, built complex employment validation rules using JBoss Drools, and designed test frameworks that decoupled upstream dependencies (including bypass modes and S3-backed mock repositories). This work helped materially reduce loan processing timelines, with pilot lenders reporting 6–20 day reductions, and the service reaching broad adoption across the lender ecosystem with significant loan volume processed through validated components.

At Barclays, as Technical Lead, he delivered the Credit Gateway platform supporting 2,000+ wealth management professionals across multiple geographies. He defined enterprise Java architecture standards, implemented secure system-to-system integration patterns, and led a critical cross-region migration of sensitive credit data with zero data loss and no business disruption—an outcome that reflects engineering discipline under the highest risk profile: customer-impacting financial operations.

Over seven years at Genworth Financial, he progressed from Software Engineer to Technical Lead on the GENIUS platform, leading a full-stack modernization while maintaining uninterrupted operations. A defining achievement was migrating workflow infrastructure from IBM MQ Workflow to IBM FileNet P8 BPM, including new workflow model design, FileNet API implementations, and a custom migration client that transitioned thousands of in-flight applications without disruption. The transformed platform delivered a 70% reduction in policy processing cycle time and 40–50% cost efficiency improvements, supporting roughly 2,000 insurance applications per week.

Across these environments, a consistent signature emerges: scale-aware architectures, migration strategies engineered for reversibility and safety, and production systems treated as accountable products—instrumented, measurable, and designed for long-term operability. Recognition such as Walmart’s Make A Difference Award, alongside certifications including AWS Certified Developer and IBM FileNet BPM/CM Developer, reinforce a career that combines hands-on technical depth with platform stewardship.