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Rekha Talla
Mid-level Full-Stack Software Engineer specializing in AI and document automation
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Backend/AI infrastructure engineer focused on production-ready LLM systems and distributed workflows. They described building a RAG-based multi-step agent with strong reliability controls, evaluation loops, and graceful degradation that improved latency by 30%, retrieval accuracy by 15%, and reduced support workload by 40%.
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Key Strengths
- Shipped production LLM retrieval and response systems
- Improved latency by 30% and retrieval accuracy by 15%
- Reduced support team manual workload by 40%
- Designed graceful degradation and fallback mechanisms to prevent hallucinations
- Built deterministic multi-step agent pipelines with structured outputs and explicit state management
- Strong production reliability mindset with batching, caching, retries, and safe defaults
- Designed offline and online evaluation frameworks for agent quality and reliability
- Handled noisy unstructured data with validation, normalization, and idempotent workflow design
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