Praveen Kumar

• Designed and implemented end-to-end AI/ML solutions supporting healthcare and financial data use cases, enabling data-driven decision making across enterprise systems • Built scalable data pipelines using PySpark, Databricks, and Airflow to process large-scale structured and unstructured datasets • Developed and deployed machine learning models for prediction, anomaly detection, and analytics using Python and Scikit-learn • Implemented Generative AI solutions using LLMs and RAG (Retrieval-Augmented Generation) for document processing, knowledge retrieval, and automation • Designed APIs and microservices (FastAPI/Flask) to integrate ML models into production applications • Applied MLOps practices using MLflow, Docker, Kubernetes, and CI/CD pipelines for model deployment, monitoring, and lifecycle management • Worked with cross-functional teams including business stakeholders, data engineering, and risk teams to deliver solutions aligned with healthcare and financial domain requirements • Optimized model performance and data workflows, improving processing efficiency and system scalability • Built dashboards and reports using SQL and BI tools to provide insights into operational and financial metrics • Ensured compliance, security, and governance standards for enterprise AI systems in regulated environments

• Developed machine learning models (regression, classification, NLP) for analytics and business insights across enterprise datasets • Built ETL and data pipelines using Python, Spark, and Airflow for large-scale data processing • Designed and deployed AI/ML solutions on Azure ML and GCP Vertex AI • Implemented NLP solutions for text classification, sentiment analysis, and document processing • Developed Generative AI and LLM-based applications for automation and knowledge retrieval • Applied MLOps practices using MLflow, Docker, and Kubernetes for scalable model deployment • Created dashboards and visualizations using Power BI/Tableau to track model performance and business KPIs • Collaborated with stakeholders to translate business requirements into data-driven solutions in healthcare and finance-related use cases

• Built and optimized machine learning models for predictive analytics and customer insights • Developed data pipelines using Python, Spark, and Airflow for large-scale data processing • Designed scalable ML systems using TensorFlow and PyTorch • Implemented NLP and AI solutions for text analytics and automation • Integrated ML models into production using APIs and cloud platforms (AWS, Azure, GCP) • Applied MLOps practices for deployment, monitoring, and continuous improvement • Delivered dashboards and reports to support business and operational decision-making • Collaborated with stakeholders to align AI solutions with business goals and analytics needs

• Developed AI/ML solutions for predictive analytics and enterprise data processing • Built data pipelines and ETL workflows using Python, Spark, and Airflow • Designed and deployed scalable ML systems for analytics and automation • Implemented NLP solutions for document processing and data extraction • Integrated AI models into applications using APIs and microservices • Applied MLOps practices for deployment, monitoring, and performance optimization • Delivered dashboards and analytics insights for operational decision-making • Collaborated with cross-functional teams to deliver data-driven solutions

• Developed AI/ML models supporting financial analytics, fraud detection, and customer insights • Built data pipelines and ETL workflows for large-scale financial data processing • Designed secure and compliant AI solutions aligned with banking and regulatory standards • Implemented NLP and AI solutions for document processing and automation • Deployed ML models on cloud platforms (AWS, Azure) using MLOps practices • Created dashboards and reports to monitor financial KPIs and model performance • Integrated AI systems with enterprise applications using APIs and microservices • Collaborated with business teams to deliver data-driven insights for financial decision-making

• Built predictive models for insurance analytics, risk assessment, and business insights • Developed data pipelines using Python, Spark, and Airflow for data processing • Designed ML systems for automation and analytics using TensorFlow and PyTorch • Implemented NLP solutions for text analytics and document processing • Deployed models using cloud platforms and MLOps frameworks • Delivered dashboards and reports for business and operational insights • Optimized model performance and data workflows for scalability • Worked with stakeholders to align AI solutions with insurance and financial domain needs