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Thanmayi Ravuri
Entry Software Engineer specializing in cloud backend and microservices
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Built production-oriented LLM agent systems for incident investigation and CRM workflows using LangGraph, FastAPI, AWS, and retrieval grounding. Stands out for treating agents like real software systems—adding schema enforcement, retries, fallbacks, monitoring, and eval loops—and tying that work to measurable gains in accuracy, latency, and analysis speed.
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Key Strengths
- Built production LLM incident investigation system end-to-end
- Improved root cause identification accuracy by 28%
- Reduced API latency by 40%
- Improved incident analysis time by 30-40%
- Reduced hallucinations by ~20-22% through retrieval grounding
- Designed reliable failure handling with retries, fallbacks, and low-confidence responses
- Architected multi-step agent workflows with strict schemas and constrained tool use
- Strong evaluation and monitoring mindset for LLM systems
- Handled messy CRM/ERP-like data with validation, normalization, and idempotency controls
- Implemented safe execution patterns with confidence thresholds and human review
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