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Anvith Reddy T
Mid-level AI/ML Engineer specializing in Generative AI and MLOps
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ML/AI engineer with hands-on ownership of fraud detection and investigator-assist systems, combining anomaly detection with RAG-based LLM summarization in production. Stands out for translating research ideas into reliable cloud-deployed workflows that improved precision to 92%, cut review time by 25-30%, and increased investigator throughput by roughly 30% while also building reusable Python infrastructure for team-wide velocity.
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Key Strengths
- Owned end-to-end production ML/AI systems from experimentation through deployment and monitoring
- Strong fraud detection and anomaly detection expertise
- Shipped RAG-based LLM summarization and Q&A workflows for investigators
- Balanced latency, cost, quality, and safety in production GenAI systems
- Built cloud-deployed ML pipelines with Docker, AWS, Azure, and CI/CD
- Implemented robust model and system monitoring including precision, recall, drift, latency, and job success rates
- Applied explainability techniques with SHAP and LIME to improve user trust
- Improved fraud model performance to 92% precision and 87% recall
- Drove 25-30% reduction in case review time
- Increased investigator throughput by about 30% without additional headcount
- Built reusable Python service/library architecture that reduced new workflow setup from 7 days to 1-2 days
- Improved adoption and trust by converting LLM outputs into structured, evidence-grounded summaries
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Experience
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Certifications
Publications
1 publication
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