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Anurag Reddy
Mid-level Data Scientist specializing in ML, MLOps, and Generative AI
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About
ML/NLP engineer who built a RAG-based technical assistant for Caterpillar field engineers, transforming PDF keyword search into intent-based semantic retrieval across manuals, logs, sensor reports, and technician notes. Strong in productionizing data/ML systems (Airflow, PySpark) with rigorous preprocessing, entity resolution, and evaluation—delivering measurable gains in accuracy, relevance, and duplicate reduction.
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Key Strengths
- Built RAG-based technical assistant for field engineers using semantic retrieval over unstructured corpora
- Designed 4-step document preprocessing pipeline (OCR normalization, dedup/versioning, segmentation, quality scoring) improving embedding quality and boosting accuracy by >20%
- Hybrid entity-resolution pipeline with supervised validation; improved pairwise F1 by ~30% and reduced duplicate entities by ~40%
- Fine-tuned embedding model on domain data; improved top-3 search relevance by ~25% and reduced irrelevant matches
- Production-grade Python workflow practices (Airflow, PySpark, modular design, tests, logging/alerting, stateless scaling)
- Structured prototype-to-production process using temporal CV and shadow deployment for anomaly detection (LSTM) outperforming rules engine by ~20%
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