Krishna YadavSenior Backend Engineer

Java · Spring Boot · Microservices · Kafka · MongoDB · Redis · Distributed Systems · HLD · LLD · Bangalore

• Owned microservices and backend services from design through delivery, building Java and Spring Boot services with Kafka event-driven architecture, serving 10,000+ users and cutting manual effort by 70%.
• Drove technical design discussions and architecture reviews, influencing system architecture and technical decisions across teams for a Spring Boot integration between Dell Live Optics and PowerSizer, cutting manual effort by 80%.
• Built AISizer from scratch and tuned it for high availability and fault tolerance, using Redis caching and MongoDB data modeling to maintain performance, delivering 40% faster deal velocity and $2M in annual savings.
• Participated in on-call production support, driving root cause analysis for critical incidents and improving service reliability while mentoring 3 junior engineers through code reviews and collaborating with Product, QA, SRE and Security teams.

Java · Python · NLP · LLM · Kafka · Microservices · Bangalore

• Shipped an agentic AI conversational workflow with intent-level analytics and an async Kafka pipeline processing 5M+ events/day, integrating NLP + LLM with Java microservices — +12% intent accuracy, +18% enterprise engagement, plus model-quality feedback loops.

Java · Python · NLP · Microservices · Dehradun

• Delivered 15+ production-grade backend/NLP systems and code-reviewed across 200+ engineers; enforced coding standards, testing patterns, observability (Grafana/Prometheus) and agile delivery.
• Taught Data Structures, Algorithms, Operating Systems and Distributed Systems to 300+ students; mentored 5+ junior engineers on system design, concurrency and production debugging.

Java · AWS · SQL · Data Modeling · Message Driven · Data Pipelines · Bangalore

• Competitor Governance: Led a team of 3 to build a recommendation engine from scratch, cutting 90 days of manual review to 3 hours with $1B+ business impact to Amazon Retail.
• Orchestrated 100+ batch-processing data pipelines using SQL and multi-threaded Apache Spark jobs on AWS EMR over ~20M records across 1,000+ metrics, solving big data and data integrity challenges and troubleshooting failures across environments.
• Implemented single-click orchestration of worldwide competitor status updates for 5M+ records across 40 metrics.
• Developed a data-driven full-stack ReactJS web application with automated end-to-end, unit and integration testing on DynamoDB, S3 and REST APIs, cutting 4 weeks of manual review to 30 minutes.