Hi, I'm Sree Korabu, an AI/ML Engineer with 3+ years of experience building and deploying ML solutions in healthcare, pharma, retail, and finance. I specialize in predictive modeling, NLP, anomaly detection, and deep learning using PyTorch, TensorFlow, XGBoost, and LSTMs. I have a proven track record of reducing pharma stock-out risks, improving fraud detection, and delivering data-driven insights that inform business decisions. I engineer production-ready ML systems in the cloud and at scale, using AWS SageMaker, Docker, Kubernetes, and CI/CD pipelines, with governance and monitoring via Prometheus and Weights & Biases. I enjoy collaborating with data engineers to design ETL pipelines in Snowflake and BigQuery, and building NLP pipelines with BERT and GPT-based models to process unstructured clinical notes and extract actionable signals.

Sree Korabu

Hi, I'm Sree Korabu, an AI/ML Engineer with 3+ years of experience building and deploying ML solutions in healthcare, pharma, retail, and finance. I specialize in predictive modeling, NLP, anomaly detection, and deep learning using PyTorch, TensorFlow, XGBoost, and LSTMs. I have a proven track record of reducing pharma stock-out risks, improving fraud detection, and delivering data-driven insights that inform business decisions. I engineer production-ready ML systems in the cloud and at scale, using AWS SageMaker, Docker, Kubernetes, and CI/CD pipelines, with governance and monitoring via Prometheus and Weights & Biases. I enjoy collaborating with data engineers to design ETL pipelines in Snowflake and BigQuery, and building NLP pipelines with BERT and GPT-based models to process unstructured clinical notes and extract actionable signals.

Available to hire

Hi, I’m Sree Korabu, an AI/ML Engineer with 3+ years of experience building and deploying ML solutions in healthcare, pharma, retail, and finance. I specialize in predictive modeling, NLP, anomaly detection, and deep learning using PyTorch, TensorFlow, XGBoost, and LSTMs. I have a proven track record of reducing pharma stock-out risks, improving fraud detection, and delivering data-driven insights that inform business decisions.

I engineer production-ready ML systems in the cloud and at scale, using AWS SageMaker, Docker, Kubernetes, and CI/CD pipelines, with governance and monitoring via Prometheus and Weights & Biases. I enjoy collaborating with data engineers to design ETL pipelines in Snowflake and BigQuery, and building NLP pipelines with BERT and GPT-based models to process unstructured clinical notes and extract actionable signals.

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Experience Level

Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
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Language

English
Fluent

Work Experience

AI/ML Engineer at McKesson
November 1, 2024 - Present
Engineered end-to-end demand forecasting models using XGBoost and LSTMs to predict pharmaceutical supply requirements, reducing stock-out risks across distribution centers. Built a real-time anomaly detection pipeline on streaming supply chain data with Dask and PyTorch, enabling early irregularity and fraud detection. Developed NLP pipelines with BERT and GPT-based models to process unstructured clinical notes for adverse drug reactions and patient feedback. Implemented automated CI/CD workflows with GitHub Actions, MLflow, and FastAPI for AWS SageMaker deployments, shrinking rollout time from weeks to days. Established monitoring and drift detection with Prometheus and Weights & Biases to strengthen governance and compliance in healthcare AI. Collaborated on ETL pipelines in Snowflake and BigQuery integrating patient, drug, and logistics datasets.
ML Engineer at Infinite Infolab
January 1, 2021 - July 1, 2023
Designed image classification with CNNs and object detection with YOLO for automated retail inventory recognition. Implemented time-series forecasting with Prophet and LSTMs for financial clients. Built personalized recommender systems using collaborative filtering and matrix factorization. Deployed chatbots with BERT and GPT-2 for scalable customer support. Optimized ETL workflows with Pandas, SQL, and MongoDB; accelerated deep learning training with Dask and GPU acceleration. Deployed production ML apps using Docker, Kubernetes, and Flask APIs in cloud environments.

Education

Master of Science in Information Systems Management at Robert Morris University
January 11, 2030 - May 1, 2025
Master of Science in Information Systems Management at Robert Morris University
January 11, 2030 - May 1, 2025
Master of Science in Information Systems Management at Robert Morris University
January 11, 2030 - May 1, 2025
Master of Science in Information Systems Management at Robert Morris University
January 11, 2030 - May 1, 2025

Qualifications

Add your qualifications or awards here.

Industry Experience

Healthcare, Life Sciences, Retail, Financial Services, Professional Services, Software & Internet