Rahul Manne

About

JavaScript/React/TypeScript engineer with hands-on open-source experience improving a hooks utility library—fixed a reported async race condition that reduced unexpected re-renders and added a debounced callback hook that became widely used. Brings a production-minded approach to performance and abstractions (APM/metrics-driven, DB/caching focus) with strong testing, documentation, and community support practices.

Experience

Education

Key Strengths

• Open-source React/TypeScript contributions (bug fixes, performance improvements, tests, docs)
• Diagnosed and fixed race conditions in async React hooks using reproducible debugging and regression tests
• Built a debounced callback hook that became a frequently used utility in the library
• Methodical performance optimization approach using architecture review plus APM/logs/metrics to find hotspots
• Risk-managed refactoring (baseline tests, incremental changes, contract stability, monitoring, feature flags/rollouts)
• Strong developer documentation practice (consumer-first READMEs, tested examples, iterative improvements via feedback)
• Effective community/user support loop: reproduce → isolate → fix → test → document → communicate
• Designed backend services and near-real-time data pipelines for AI-powered automation platform
• Improved reliability of distributed service communication using retries (exponential backoff) and timeout handling
• Built observability via structured logging and monitoring to detect/isolate failures
• Scaled systems for traffic/data spikes using stateless services and horizontal scaling
• Performance tuning through database query/index optimization and caching
• Debugged real-time pipeline latency via instrumentation/metrics and bottleneck isolation; addressed concurrency contention and out-of-order data
• Owned and operated a production .NET Core REST API on Azure for internal business workflows
• Diagnosed and resolved peak-traffic latency/timeouts via SQL tuning, async refactors, and caching (~35% response-time improvement)
• Systematic performance debugging using layered timing instrumentation, request logs/metrics correlation, and DB execution-plan analysis
• Shipped an end-to-end anomaly detection feature integrating a Python/scikit-learn model into a .NET API with CI/CD
• Production ML evaluation and monitoring (precision/recall, threshold tuning, anomaly-rate monitoring, drift detection signals, user feedback loops)
• Designed reliable multi-step backend workflows with transient/permanent error classification, retries with backoff, DLQ, and auditability
• Strong operational practices: structured logging, correlation IDs, testing strategy, dependency pinning