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Uday Chilakala
Mid-level Machine Learning Engineer specializing in NLP, computer vision, and RAG systems
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About
Machine learning/NLP engineer who built a production-oriented retrieval-based AI system at Morgan Stanley for healthcare use cases, combining RAG over unstructured patient records with deep-learning medical image segmentation (U-Net/Mask R-CNN). Strong in end-to-end pipelines and MLOps (Spark/MongoDB, AWS SageMaker, CI/CD, monitoring, automated retraining) and in entity resolution/data quality validation for noisy clinical data.
Experience
Education
Key Strengths
- Built retrieval-based AI system linking unstructured patient records with medical image segmentation outputs
- Applied RAG with domain-specific transformer models (BioBERT/ClinicalBERT) to improve medical data linking accuracy
- Production deployment and scalability via AWS SageMaker with optimized inference pipelines
- Designed robust multimodal data pipelines (text + images) using Spark and MongoDB
- Implemented production MLOps: monitoring, CI/CD, and automated retraining for reliability
- Designed and validated data cleaning/entity resolution workflows using NER, fuzzy matching, and labeled validation sets
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