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Vikranth Gurram
Machine learning engineer and software developer with experience across fintech, e-commerce, and gaming.
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ML/AI engineer with hands-on ownership of production systems spanning classical ML fraud detection and GenAI agent workflows. At Fidelity, they built an end-to-end fraud platform that improved review queue Precision@K by 15-20% while reducing false positives 10-15%, and they also shipped RAG-based agent systems that cut manual workflow effort by 30-40%.
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Key Strengths
- Owned end-to-end production fraud detection system
- Strong ML systems engineering from data ingestion to deployment and monitoring
- Fraud detection expertise with point-in-time correct feature engineering
- Improved Precision@K for manual review queue by 15-20%
- Reduced false positives in top-ranked transactions by 10-15%
- Shipped production LLM agent workflow using RAG and tool calling
- Improved workflow efficiency by reducing manual effort 30-40%
- Translates research-style GenAI approaches into reliable production systems
- Balances latency, cost, quality, and safety through pragmatic architecture tradeoffs
- Built reusable Python/FastAPI services consumed across teams
- Improved reliability and reduced duplication through shared feature and inference services
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