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Prasad Sadineni
Mid-level AI Engineer specializing in LLM fine-tuning, RAG, and agentic systems
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About
Building and deploying production in-house, domain-specific LLM chatbots for enterprises that cannot use third-party GPT tools due to internal policies. Focused on reducing latency and improving domain awareness using fine-tuning, continual learning, and advanced RAG/agent retrieval strategies, with experience orchestrating multi-agent workflows via LangChain/LlamaIndex and vector DBs (FAISS, Weaviate, Chroma).
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Key Strengths
- Built and deployed in-house domain-specific chatbots for companies with restrictions on third-party GPT usage
- Improved RAG performance via custom retrieval strategies and agent fallback when context is insufficient
- Hands-on orchestration of real-time multi-agent pipelines using LangChain and LlamaIndex with FAISS/Weaviate/Chroma
- Model adaptation experience (supervised + unsupervised fine-tuning and continual learning)
- Evaluation-driven approach using latency, accuracy, and faithfulness metrics
- Reported ~90–95% system accuracy
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