Laharika Kanchanapalli

About

Backend/ML platform engineer in the insurance domain who built and shipped an AI-driven risk scoring/fraud detection service for underwriting. Runs containerized .NET Core and Python inference services on Azure (AKS + GPU nodes) with Terraform/ARM and Azure DevOps CI/CD, and has hands-on experience improving reliability under peak load plus implementing production AI guardrails (drift monitoring, fallbacks, human review, audit logs).

Experience

Education

Key Strengths

• Built and operated production AI inference APIs on AKS with GPU nodes
• Debugged and resolved peak-load latency/timeouts via memory optimization, right-sized pod resources, and Redis caching
• End-to-end ownership of containerization, deployment, CI/CD, autoscaling, monitoring, and performance tuning
• Designed multi-step policy processing workflows with verification gates and automatic escalation to human review on repeated failures
• Strong production engineering practices in Python: clean layering, typing, testing, structured logging, and observability
• Relational data modeling and query optimization using indexing and query rewrites based on query plan analysis
• Implemented AI production guardrails: audit logs, drift monitoring, fallbacks, and human-in-the-loop escalation for anomalous outputs