Chandra Shekar Akkandra

About

Finance-domain ML/LLM engineer who has shipped production systems including a RAG-based financial insights assistant with a custom post-generation validation layer that verifies atomic claims against retrieved source text to prevent hallucinations in compliance-critical workflows. Also built large-scale MLOps automation on AWS using Kubeflow + MLflow + CI/CD for fraud detection and credit risk models processing 500M+ transactions/day with a 99.99% uptime goal, and partnered closely with JP Morgan risk/compliance stakeholders on NLP-driven compliance monitoring.

Experience

Education

Key Strengths

• Built and deployed a RAG-based financial insights assistant for analysts, reducing manual research time
• Designed a validation layer to mitigate LLM hallucinations via atomic-claim extraction and semantic entailment checks with source traceability
• Architected end-to-end MLOps platform (Kubeflow + MLflow + AWS CI/CD) for frequent retraining/validation/deployment
• Experience operating at very large scale (fraud/credit models processing 500M+ transactions daily) with 99.99% uptime target
• Reliability-focused engineering: modular pipeline design, unit/integration testing (pytest), load testing on Kubernetes (Locust), and KPI monitoring
• Effective collaboration with non-technical risk/compliance/legal stakeholders; simplified labeling and reporting to improve model precision and reduce false alerts