Amal Singh Bhadauria

Built and maintained AI-enabled, real-time data extraction and ingestion pipelines in Python, processing millions of financial records daily for global banking and fintech clients across UAE, South Africa, Hong Kong, Tanzania, Rwanda, and Nigeria. Designed and developed a reusable, plug-and-play multilingual Unicode processing library using NLP-based text normalization, eliminating hardcoded customer-specific logic and reducing manual fixes across the extraction platform; adopted by multiple engineering teams. Architected a configurable, rule-based fraud detection and suspicious-transaction alerting framework supporting dynamic pattern matching, real-time notifications, and automated reporting, reducing manual verification effort. Migrated manual data extraction processes to automated, real-time pipelines using Python and DynamoDB, improving processing throughput by 40%. Reduced onboarding time for new financial institutions from 1 week to 1-2 days by replacing manual scripts with scalable, AI/LLM-driven automation and open-source solutions. Improved team workflow efficiency by 30% and reduced manual intervention by 25% through process optimization, automation tooling, and elimination of production bottlenecks. Enhanced observability across production systems with intelligent logging, monitoring, and failure-detection mechanisms, improving platform stability. Mentored and onboarded new engineers by building automated onboarding tools and leading technical knowledge-sharing sessions, accelerating ramp-up time and developer productivity. Collaborated cross-functionally with engineering teams to standardize extraction logic, build reusable APIs, and support CI/CD automation and production rollouts. Recognized with Best Employee Award (2023) and Pat on the Back Award (2022) for technical contributions and impact.