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Jeet Choksi
Mid-level Machine Learning Engineer specializing in real-time AI and data platforms
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About
ML/NLP engineer who has built production systems end-to-end: a real-time recommendation platform (100k+ profiles) using BERTopic-style clustering and a RAG-based news summarization/recommendation stack with ChromaDB. Strong focus on scaling and reliability (GPU batching, Redis caching, Kafka ingestion, Docker/Kubernetes, Prometheus/Grafana) and on maintaining model quality over time via drift monitoring and retraining triggers.
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Key Strengths
- Built and productionized ML/NLP recommendation system processing 100k+ user profiles
- Deep expertise in topic modeling pipeline design (Sentence-BERT + UMAP + HDBSCAN/BERTopic)
- Scaled embedding generation with batching and GPU acceleration
- Reduced latency by 25%+ via Redis caching
- Production-grade MLOps/observability (Docker/Kubernetes + Prometheus/Grafana monitoring)
- Handled topic drift with monitoring and retraining triggers based on distribution-shift thresholds
- Entity resolution combining rule-based matching with embedding-based fuzzy matching and validation heuristics
- Improved search relevance using vector DB retrieval (ChromaDB), contrastive fine-tuning, and cross-encoder re-ranking
- Pragmatic prototype-to-production decision-making using metrics stability, user feedback, and cost/throughput constraints
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2 publications
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