Sachin Gupta
Technical Leader at eBay
San Jose, California, United States
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I am a senior backend and platform engineer at eBay with over 14 years of experience building and modernizing enterprise systems. I work on large scale billing and financial platforms that demand high reliability, strong security, and precise data correctness. I spend most of my time modernizing legacy architectures, designing cloud ready services, and building APIs and pipelines that hold up under heavy traffic and real world failure modes.
Lately, I have been integrating AI into engineering workflows, focusing on diagnostics, observability, testing, and automation that help teams move faster without sacrificing safety.
On stage, I share field tested patterns and mistakes to avoid, from migrations and cutovers to incident driven design and operational readiness. My goal is to give engineers practical takeaways they can apply immediately when building and modernizing systems at scale.
Area of Expertise
Topics
Microservice Cognitive Index for Deploy Diagnosis and Change Impact
Modern cloud native systems often span hundreds of microservices, thousands of endpoints, and fragmented telemetry across logs, traces, metrics, deployments, and service catalogs. Even with strong observability, engineers still struggle to answer two high impact questions fast: why did this deployment fail, and if I change this service or API, what breaks.
This industry session presents an AI powered Microservice Cognitive Index, an intelligence layer on top of existing observability. It builds a canonical evidence graph by ingesting telemetry, deriving runtime topology from traces, clustering incident signatures from normalized logs, correlating regressions with deployments, and propagating change impact through dependency and contract signals. It combines graph based reasoning with machine learning and large language models to summarize evidence, rank likely causes, and explain blast radius with confidence. Unlike chat with logs approaches, it enforces tool grounded answers with evidence references, confidence scoring, and refusal policies when data is incomplete or confounded, making results auditable and safer for operations.
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