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