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Ramya Jonnala
Principal/GenAI Engineer specializing in LLMs, RAG, and MLOps
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About
Built a production AI-powered university marking system that automates question generation and grading from PDF course materials using a RAG pipeline (S3 + Pinecone) orchestrated with LangChain/LangGraph and deployed on AWS ECS via Docker/ECR and GitHub Actions CI/CD. Addressed a key real-world LLM challenge—grading consistency—by implementing rubric-based scoring, retrieval re-ranking, and standardized context summarization, validated against human instructors.
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Key Strengths
- Built and deployed production LLM system end-to-end (RAG + containerization + AWS ECS)
- Designs reliable multi-step agent workflows with validation and fallback logic
- Improves LLM output consistency via rubric-based scoring decomposition
- Strong retrieval quality focus (chunking, re-ranking, context normalization) for factual grounding
- Pragmatic model/prompt selection using experimentation, A/B tests, and cost-awareness
- Testing discipline for LLM systems (unit + end-to-end + edge cases) with human-in-the-loop evaluation
- Effective collaboration with non-technical stakeholders (professors) and iterating prompts from feedback
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