Mahesh Kota

About

Backend engineer with production experience building and scaling a Java/Spring Boot payment processing API on AWS (PostgreSQL/Redis) handling a few thousand RPS, including deep performance debugging (connection exhaustion) and observability (CloudWatch, Actuator, Zipkin). Also shipped application-layer AI features (OpenAI email summarizer with feedback loop, ~40% faster agent response times) and designed reliable multi-step workflow orchestration with retries and manual escalation, plus strong SQL tuning and Python engineering practices.

Experience

Education

Key Strengths

• Debugged high-traffic latency by identifying DB connection leaks and implementing HikariCP connection pooling
• Scaled a payment processing API to a few thousand requests per second on AWS with caching and observability
• Built monitoring/alerting using CloudWatch + Spring Actuator metrics and tracked cache hit ratios/DB performance
• Implemented distributed tracing (Zipkin) to detect and diagnose slow requests
• Shipped an OpenAI-powered email summarization feature end-to-end (integration, prompt tuning, feedback loop) reducing support response time ~40%
• Designed fault-tolerant multi-step workflow orchestration with queue-based workers, persisted state machine, retries, and manual escalation
• Improved PostgreSQL query performance from ~8s to <300ms using EXPLAIN ANALYZE, composite indexes, and denormalized summary tables
• Established Python maintainability practices: typing (mypy), lint/format (flake8/black), async (asyncio/aiohttp), background jobs (Celery), and testing (pytest in CI)
• Designed AI production guardrails: observability, evaluation pipelines, drift monitoring, rollback, fallbacks, and human-in-the-loop review