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Prudhvi Yalamanchili
Mid-level Software Development Engineer specializing in AWS telemetry and DDoS mitigation
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About
Amazon engineer who built an Amazon Bedrock-powered summarization layer over large-scale network/service telemetry (“top talker” insights) to help security engineers triage anomalies faster. Emphasizes production-grade design patterns for LLM features—non-blocking enrichment, deterministic fallbacks, strict structured outputs, and monitoring to preserve trust in source-of-truth telemetry.
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Key Strengths
- Designed LLM enrichment as non-blocking layer to avoid single point of failure
- Strong reliability mindset: timeouts, retries, rate limits, monitoring, and fallbacks
- Bounded/predictable LLM behavior via structured outputs and controlled context
- Applied LLMs to large-scale telemetry to speed security/network triage and interpretation
- Evaluation approach combining offline scenario tests with online operational metrics
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