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Thota Rajkumar
Junior Cloud & AI Infrastructure Engineer specializing in Agentic AI and AWS
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About
Built and deployed a production AI career-advice agent designed to combat unreliable/generic LLM guidance by grounding outputs in retrieval-first RAG over resumes/job/hiring data, with multi-step reasoning, structured memory, and evidence-only prompting to reduce hallucinations. Implemented the system with LangChain/Python and deployed on AWS as scalable microservices orchestrated via REST and asynchronous calls, iterating closely with career coaches and students.
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Key Strengths
- Built and deployed an AI career agent to production
- Designs LLM workflows grounded in retrieval-first RAG to improve factual reliability
- Implements multi-step reasoning loops with clarifying questions and iterative refinement
- Uses prompt constraints and evidence-only citation to reduce hallucinations
- Modular agent design with measurable evaluation (accuracy/correctness/latency) plus qualitative review
- Production-oriented architecture on AWS with independently scalable microservices and async REST orchestration
- Effectively translates non-technical stakeholder needs (career coaches/students) into AI workflows via prototyping and iteration
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