Resume

Built and scaled an end‑to‑end document AI and automation platform for financial receivables, slashing manual effort by 30% through LLM‑powered document extraction, classification, and workflow orchestration across invoices, payment statements, and credit documents, supported by scalable annotation pipelines and data quality frameworks.

Architected real‑time ML‑based financial risk profiling models leveraging internal transaction signals and external credit data to detect anomalies, boosting cash flow by 30% and reducing bad debt by 20% through production‑grade MLOps pipelines deployed on AWS SageMaker with automated retraining and drift monitoring.

Led a 100+ multidisciplinary engineering, ML engineering, and data science team across multi‑city matrix environments, fostering a growth‑focused culture that achieved a consistent 4.5/5 manager satisfaction score while delivering multiple high‑impact BFSI automation products spanning lending operations, compliance, and financial decisioning.

Established AI governance and model evaluation best practices for financial ML systems, implementing explainability frameworks, bias detection, and regulatory compliance guardrails that became organizational standards for responsible AI deployment in high‑stakes BFSI use cases.