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Shanmukha Jayavarapu
Mid-level AI/ML Engineer specializing in fraud detection and healthcare predictive analytics
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Built and deployed a production LLM-powered calorie-counting chatbot that turns plain-English meal descriptions into normalized food entities, quantities, and calorie estimates using a hybrid transformer + rule-engine pipeline. Emphasizes reliability with schema/constraint guardrails, confidence-based routing (including embedding similarity search fallbacks), and strong observability/metrics (hallucination rate, calibration, latency, cost). Partnered closely with nutritionists to encode domain standards into mappings and validation logic.
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Key Strengths
- Built and deployed a production LLM-powered calorie counting chatbot converting free-text food logs into structured nutrition data
- Hybrid architecture combining transformer NLP with deterministic rule-based validation for safety/correctness
- Strong guardrails design (schema/type checks, unit normalization, realistic bounds) to mitigate probabilistic outputs and hallucinations
- Orchestrates multi-stage LLM workflows with modular components, observability, and error isolation
- Latency/cost optimization via confidence-based conditional routing and fallbacks (skipping heavy retrieval when high confidence)
- Metrics-driven evaluation across model and system KPIs (hallucination rate, calibration, latency, cost per request, drift monitoring)
- Effective cross-functional collaboration translating nutritionist domain expertise into validation rules and entity mappings
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