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Likitha Kukunarapu
Mid-level Applied AI Engineer specializing in data engineering and healthcare AI
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Built production LLM agents spanning document Q&A, financial insight generation, and ERP-like operational data workflows, with a strong focus on reliability, grounding, and evaluation. Stands out for translating LLM systems into measurable business outcomes, including 70%–80% support workload reduction and a fallback-rate improvement from 18% to 8% through targeted RAG iteration.
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Key Strengths
- Shipped production RAG agent with measurable support impact
- Reduced support workload by 70%–80%
- Built hallucination detection and safe fallback mechanisms
- Designed end-to-end multi-step agent workflows with tool calling and structured outputs
- Strong reliability engineering for LLM systems
- Improved agent performance through evaluation-driven iteration
- Cut fallback rate from 18% to 8% via layout-aware chunking
- Experience with financial-data insight generation pipelines
- Handled messy ERP-like data with canonical schemas and idempotent safeguards
- Grounded LLM outputs with validation and faithfulness checks
Reference Highlights
- good understanding of AI
- able to use GenAI to build agentic AI tools
- able to evaluate model performance
- designed metrics to improve the model
- achieved over 80% on company identification/data extraction
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