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Keerthi Kalluri
Senior Full-Stack & GenAI Engineer specializing in healthcare and financial services
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Built and deployed a production LLM-powered customer support assistant using a RAG backend in Python, focused on deflecting repetitive Tier-1 tickets and reducing resolution time. Demonstrates strong production engineering instincts around reliability (confidence scoring + human fallback), scalability/cost optimization (multi-stage pipelines), and workflow orchestration/observability (LangChain, custom DAGs, structured logging, step metrics).
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Key Strengths
- Built and deployed a production LLM customer support assistant to reduce ticket resolution time and deflect repetitive Tier-1 queries
- Improved LLM reliability by enforcing RAG-only answering, confidence scoring, and fallback to human/no-answer
- Cost/latency optimization at scale via multi-stage pipelines and prompt reduction
- Designs modular, idempotent LLM workflows with clear I/O for easier debugging and maintenance
- Strong production observability practices (structured logging, step-level metrics, timeouts, graceful fallbacks)
- Pragmatic model/retrieval selection balancing accuracy, latency, cost, and data type
- Effective collaboration with non-technical stakeholders to define success metrics and fallback rules
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