ANSHUL MANYAM

Took full ownership of the system design for a centralized master data service using Java (Spring Boot 3) and Redis, cutting API response times by 40% to directly support core business operations.

Architected a high-availability payment gateway leveraging PostgreSQL, JPA, and Liquibase, demonstrating mastery of software quality via rigorous unit testing to achieve 95% coverage and a 99.9% success rate.

Constructed a resilient distributed system for live file request monitoring, utilizing RabbitMQ and Kafka to ensure 99% availability with automated retry mechanisms and fault tolerance.

Instrumented end-to-end observability across all microservices utilizing Datadog, configuring real-time telemetry and automated alerting to proactively resolve system bottlenecks.

Modernized release cycles by implementing CI/CD pipelines (Git) and deploying containerized microservices via Docker, Kubernetes, and Helm, eliminating environment inconsistencies.

Fortified platform security by integrating Vault and IAM protocols for granular user authorization, actively driving team contribution through rigorous code reviews and architectural evaluations.

Engineered scalable batch processing pipelines using Python 3.x, handling complex data formats (JSON/Parquet) for millions of records and boosting backend efficiency by 30%.

Synchronized stream processing workflows using Kafka, Spring MVC, and REST APIs, drastically reducing data latency and resolving critical system instability issues.

Defined robust data modeling and schema design strategies for PostgreSQL and MongoDB, automating all database schema migrations across distributed systems.

Designed and deployed a scalable, event-driven microservices architecture handling simulated high-throughput processing of 1,000+ events/second.

Implemented robust fault tolerance using retry mechanisms (exponential backoff) and a Dead Letter Queue (DLQ), ensuring 100% message recovery and zero data loss for failed events.

Integrated comprehensive observability via Prometheus, Grafana, and the ELK Stack to monitor 15+ JVM metrics and real-time processing rates, accelerating issue resolution by 40%.

Containerized the distributed system with Docker Compose, achieving <2-minute local deployment times, and hosted APIs on Render and Railway cloud platforms.

Facilitated 10K+ concurrent users with <100ms latency using scalable WebSocket architecture and responsive backend design.

Optimized MongoDB to handle 1M+ messages/day with 30% faster read operations, effectively managing high-throughput data streams.

Improved deployment efficiency by 60% with Docker and automated CI/CD pipelines.

Achieved 93% detection accuracy across diverse object categories by developing an object detection system using the SSD algorithm and Machine Learning.

Accelerated neural network performance, optimizing processing time by 30% for real-time object detection.