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prashanth Jamalapurapu
Mid-level AI/ML Engineer specializing in data engineering, LLM/RAG pipelines, and recommender systems
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Research assistant at St. Louis University who built and deployed a production document-intelligence RAG system (Python/TensorFlow, vector DB, FastAPI) on AWS, focusing on grounding to reduce hallucinations and latency optimization via caching/async/batching. Also developed a personalized recommendation system for the Frenzy social platform and partnered closely with product/UX to define metrics and iterate on hybrid recommenders and cold-start handling.
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Key Strengths
- Built and deployed production RAG document intelligence system (FastAPI on AWS EC2 with S3, Dockerized)
- Improved answer grounding by tuning retrieval (chunking, similarity thresholds, metadata filtering) to reduce hallucinations
- Performance/latency optimization for LLM systems (caching, async calls, batching)
- End-to-end ML/LLM workflow orchestration with Apache Airflow (including automated retraining)
- Strong evaluation discipline: unit/integration tests, benchmarks, task-appropriate metrics, staging before production, monitoring/feedback loops
- Pragmatic model/prompt selection using A/B testing to balance accuracy, latency, and resource usage
- Effective cross-functional collaboration translating non-technical feedback into recommender-system improvements (hybrid weighting, cold-start fixes)
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