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Raj Kalwar
Mid-level Full-Stack Java Engineer specializing in FinTech and digital payments
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About
Built and shipped an LLM-powered support assistant for a fintech payment reconciliation system that automated failed-transaction analysis at scale. Delivered measurable production outcomes (40% less manual reconciliation, 25% better detection accuracy, 99%+ uptime) and implemented strong reliability patterns (Prometheus/Grafana monitoring, retries/fallbacks, idempotency, Resilience4j circuit breakers) plus iterative retraining driven by real error analysis.
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Key Strengths
- Shipped LLM support assistant into fintech payment reconciliation production system
- Reduced manual reconciliation time ~40% via automated categorization and suggested resolutions
- Improved identification accuracy for duplicate/incomplete payments ~25%, reducing discrepancies and complaints
- Achieved 99%+ service uptime with monitoring, retries, and fallback logic during peak hours
- Designed multi-step workflow with modular functions (extraction, classification, validation, recommendation)
- Built production monitoring and feedback loops (Prometheus/Grafana + offline eval vs analyst resolutions)
- Materially improved model reliability via error analysis and feature addition (retry counts), boosting accuracy ~30%
- Handled messy ERP-like data with validation, staging-based cleaning, idempotency, and circuit breakers (Resilience4j)
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