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Nayana Nagarajappa
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps
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Software engineer currently building AI-powered backend systems for interview analysis, with end-to-end ownership of an LLM-based monitoring platform. Stands out for combining practical product delivery in an ambiguous early-stage environment with measurable impact: over 40% reduction in manual review effort and roughly 20% lower inference cost.
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Key Strengths
- Owned an AI interview monitoring backend end-to-end from design to operations
- Reduced manual review effort by over 40%
- Cut inference cost by about 20% through prompt and token optimization
- Strong judgment on async architecture tradeoffs for reliability and latency
- Built modular AI workflows in ambiguous early-stage environments
- Improved production AI reliability with structured validation and fallback logic
- Hands-on incident ownership for LLM output/parsing failures
- Focus on observability and monitoring for production AI systems
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