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Hari Chandana Kasula
Entry Machine Learning Engineer specializing in NLP, computer vision, and recommender systems
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About
Built and shipped an end-to-end podcast recommendation system exposed via a Flask API and React UI, explicitly balancing relevance, diversity (MMR), and safety constraints while meeting ~200ms latency targets. Also implemented a production-style RAG/information-extraction pipeline using web retrieval, spaCy NER, and fine-tuned SpanBERT with guardrails and evaluation loops (precision/recall/F1) to tune confidence thresholds and improve reliability.
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Key Strengths
- Owned end-to-end feature delivery (problem definition → architecture → implementation → launch → iteration)
- Designed recommendation pipeline balancing relevance, diversity, and safety constraints
- Latency-aware system design (kept API responses under ~200ms via candidate limiting and precomputed embeddings)
- Data-driven iteration using explicit/implicit user feedback signals and stage-level logging
- Built production-style RAG/IE pipeline with guardrails (confidence thresholds, structured output validation)
- Evaluation-driven improvements using labeled test sets and precision/recall/F1 tradeoffs
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Experience
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1 publication
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