AI/ML Engineer with 4+ years of experience designing, developing, and deploying scalable Machine Learning, Generative AI, and LLM-powered applications across healthcare and enterprise environments in the US. Proven exper…
AI/ML Engineer with 4+ years of experience designing, developing, and deploying scalable Machine Learning, Generative AI, and LLM-powered applications across healthcare and enterprise environments in the US. Proven expertise in building end-to-end MLOps pipelines, real-time data systems, and production-grade AI solutions using AWS, Snowflake, Databricks, Spark, Kubernetes, and Docker. Strong hands-on experience in LLM applications (RAG, LangChain, OpenAI APIs), NLP systems, predictive modeling, and cloud-native ML deployment. Skilled in transforming complex healthcare and supply chain data into actionable insights that improve forecasting accuracy, operational efficiency, and decision-making.
Designed and deployed machine learning models for healthcare supply chain forecasting, improving inventory optimization and reducing stock-out risks across pharmaceutical distribution networks.
Built LLM-powered healthcare intelligence systems using OpenAI and LangChain to extract insights from clinical and operational documents using RAG-based architectures.
Developed scalable end-to-end ML pipelines using PyTorch, TensorFlow, and AWS for patient outcome prediction and healthcare analytics.
Implemented MLOps pipelines using MLflow and Apache Airflow for automated training, experiment tracking, and production deployment.
Engineered real-time data pipelines using AWS S3, Snowflake, DBT, Kafka, and Spark for large-scale healthcare and pharmacy data processing.
Deployed containerized ML services using Docker and Kubernetes, enabling scalable and HIPAA-compliant cloud deployment.
Applied advanced NLP techniques (BERT, Transformers, NER models) to support clinical decision systems and improve data extraction accuracy.
Developed machine learning models using Python and Scikit-learn for enterprise analytics and automation use cases.
Built and optimized data pipelines using SQL, Pandas, and Python for structured and unstructured datasets.
Developed and deployed REST APIs for ML model integration into enterprise applications on AWS cloud.
Supported model evaluation and performance tuning, improving accuracy and system reliability in production environments.
Implemented NLP-based classification systems for document automation and customer support workflows.
Assisted in development of machine learning models for classification and prediction tasks using Scikit-learn.
Performed data preprocessing, feature engineering, and dataset preparation using Pandas and NumPy.
Built basic Flask APIs for ML model deployment and backend integration.
Conducted model evaluation using accuracy, precision, recall, and F1-score metrics.
Supported ML lifecycle activities including dataset management, training, and documentation.
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