Designed and built high-throughput Java backend services supporting 150+ client portfolios and 700K+ daily transactions in low-latency financial environments

Implemented distributed microservices exposing portfolio, trade, and risk data via REST, gRPC, and GraphQL APIs

Integrated C++ analytics engines with Java services to optimize latency-critical paths

Engineered concurrent and asynchronous pipelines using thread pools, non-blocking I/O, and event-driven patterns

Reduced p95 latency by 20–25% through profiling, JVM tuning, and memory optimization

Built resilient validation and reconciliation workflows, reducing data mismatches by 30%

Deployed and monitored containerized services using Docker and Kubernetes with auto-scaling and health checks

Developed backend platforms in Java, C++, and Python processing 2M+ enterprise records for analytics and reporting

Optimized algorithms and memory usage in Java and C++, reducing heap usage by 60%

Built multi-threaded processing modules for high-concurrency workloads with predictable performance

Redesigned PostgreSQL schemas and tuned queries using indexing and partitioning, improving throughput by 35–40%

Implemented REST services integrating 5+ enterprise systems handling 250K+ daily requests under 100ms latency

Built automated data ingestion and reconciliation pipelines, reducing turnaround time by 35%

Collaborated with product and business teams to design scalable backend architectures