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DhanushKautilya Kammaripalle
Junior AI Integration Engineer specializing in LLM agents and RAG on cloud platforms
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About
Built and deployed LLM-powered features for a startup organizational management application, focusing on real-world deployment constraints like latency and cost. Implemented RAG with FAISS and improved retrieval quality by switching embedding models (OpenAI/Hugging Face) and fine-tuning embeddings on medical corpora for a medical-report UI feature. Uses LangChain and LangGraph to orchestrate multi-node LLM API workflows and evaluates systems with metrics like latency, cost per request, and error taxonomy.
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Key Strengths
- Production deployment of LLM features with latency and cost constraints
- RAG quality optimization via model comparison, chunking, and embedding tuning
- Fine-tuned embeddings on medical corpora to improve FAISS retrieval performance
- LLM orchestration using LangChain and LangGraph (multi-node graph workflows)
- Structured evaluation approach using latency, cost per request, and error taxonomy
- Ability to align non-technical stakeholders on feasibility, cost, and latency tradeoffs
Reference Highlights
- Excellent cross-team collaboration
- Very strong with non-technical stakeholders
- Bridges gaps between business expectations and technical feasibility
- Clear and detailed communicator
- Patient listener
- Handles hurdles effectively
- Proactive problem-solving (proposed workable approach)
- Business-focused and outcome-oriented
- Hands-on AI engineer who ships solutions to production
- Structured and pragmatic approach to technology and budget efficiency
- Strong at balancing performance, scalability, and cost
- Proactive in gathering requirements and clarifying constraints
- Strong cross-team collaboration
- Takes ownership and responsibility (setup, provisioning, monitoring)
- Stays current with emerging AI features and releases
- Clear, structured communicator with non-technical stakeholders
- Breaks down complex AI topics into straightforward explanations
- Helps align technical solutions with business expectations
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