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Naga Venkata Padala
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and real-time fraud detection
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About
GenAI/ML engineer who has shipped production agentic systems in highly regulated and high-throughput environments, including an AWS Bedrock-based fraud/compliance workflow at U.S. Bank with PII redaction and hallucination detection that cut investigation time by 50%+. Also built and evaluated RAG and recommendation systems at Target, using RAGAS-driven testing, hybrid retrieval with re-ranking, and SHAP explainability dashboards to align model behavior with merchandising business KPIs.
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Key Strengths
- Deployed AWS Bedrock agentic system for fraud/compliance, cutting manual investigation time by 50%+
- Built compliance-grade GenAI safeguards (hallucination detection, PII redaction) for regulated banking use cases
- Designed low-latency real-time ML pipelines (Spark Structured Streaming sub-second scoring) at high volume
- Optimized model inference performance using ONNX and quantization
- Production MLOps/infra delivery with Terraform + FastAPI on Kubernetes with autoscaling
- End-to-end ML lifecycle automation with Airflow + MLflow (retraining, dependencies, versioning)
- Metric-driven agent/RAG evaluation using golden datasets and RAGAS to prevent regressions
- Operational monitoring for latency and concept drift using Prometheus/Grafana
- Strong retrieval strategy design (hybrid keyword+semantic search with re-ranking) to improve precision and control cost
- Effective cross-functional collaboration: aligned recommender optimization to business KPIs and delivered SHAP explainability dashboards
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