Akhila Kannegari

About

ML-focused candidate with strong Wells Fargo experience building production fraud systems and internal GenAI tools for fraud analysts. Stands out for measurable impact in fraud detection—raising recall from 71% to 88%—while also demonstrating hands-on depth across streaming infrastructure, MLOps, LLM/RAG implementation, and Python service architecture.

Key Strengths

• end-to-end ownership of production fraud detection systems
• improved fraud recall from 71% to 88% while maintaining false positive rate
• real-time ML system design for high-throughput transaction scoring
• production monitoring with recall, false positive, latency, and data drift tracking
• iterative model improvement using automated retraining workflows
• applied GenAI/RAG to fraud analyst workflows
• domain fine-tuning and safety-focused evaluation for LLM features
• scaled streaming ML pipelines to 8,000-10,000 transactions per minute
• balanced latency, cost, quality, and safety with fallback rule-based checks
• built reusable Python inference services with strong testing and CI/CD practices

Experience

Education

Skills

Languages