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Supriya Mattapelly
Mid-level AI/ML Engineer specializing in GenAI agents, RAG pipelines, and MLOps
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About
AI/ML engineer who built a production RAG-based internal document intelligence assistant (LangChain + Pinecone) to let employees query enterprise reports in natural language. Demonstrated hands-on pipeline orchestration with Apache Airflow and tackled real production issues like retrieval grounding and latency using tuning, caching, and token optimization, while partnering closely with non-technical business stakeholders through iterative demos.
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Key Strengths
- Built and deployed an enterprise document Q&A assistant using RAG (LangChain + Pinecone)
- Designed end-to-end ingestion-to-inference pipelines (chunking, embeddings, vector storage, retrieval, LLM response)
- Improved retrieval relevance/grounding via chunk-size experiments, top-k tuning, and embedding/similarity parameter optimization
- Reduced latency through caching and token/context optimization for real-time usage
- Orchestrated ML/AI pipelines with Apache Airflow (DAGs, monitoring, dependency management)
- Established evaluation/monitoring approach with metrics (quality, latency, hallucination rate, retrieval accuracy) and iterative prompt/model refinement
- Effective collaboration with non-technical stakeholders via demos, requirement translation, and feedback-driven response formatting
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