Nainesh Rathod Agentic AI & ML Engineer

Scalable ML pipelines and NLP model optimization.

• Created an automated system to analyze customer sentiment from large volumes of text, helping the marketing team identify key trends and improve campaign strategies with 85% accuracy.
• Streamlined the software release process by automating manual tasks, drastically cutting down the time to deploy new features from several hours to just under 10 minutes.
• Architected an end-to-end MLOps pipeline on AWS using CodePipeline and SageMaker, integrating MLflow for experiment tracking and Docker for reproducible model containerization.
• engineered high-volume feature extraction pipelines using Databricks PySpark to process unstructured text data, fine-tuning a BERT transformer model for multi-class sentiment classification.

End-to-end ML systems and full-stack development.

• Built an AI system for medical imaging that automatically detects health anomalies, speeding up the diagnosis process by 75% and freeing up doctors to focus on patient care.
• Led a major cost-saving initiative by migrating software infrastructure to open-source systems, saving the company over $500,000 annually in licensing fees.
• Designed and trained custom YOLOv5 object detection models on Amazon SageMaker for medical anomaly detection, optimizing inference pipelines for real-time edge deployment.
• Developed an event-driven file ingestion service using AWS Lambda and S3 to handle 5000+ daily multipart uploads, integrating with a React/Redux frontend for seamless medical record management.