Leveraged Python extensive libraries (Pandas, NumPy, TensorFlow) to automate data pipelines and build machine learning models, improving data processing and prediction capabilities in projects.

Utilized R for statistical analysis and visualization, extracting actionable insights that enhanced decision-making in research and business-focused projects.

Designed and optimized complex SQL queries, facilitating efficient data retrieval and real-time reporting for large-scale enterprise applications.

Developed robust backend services using C# for enterprise-level applications, improving performance and scalability in cloud-based systems.

Built and enhanced user interfaces with HTML5, improving usability and user experience in web-based projects.

Implemented GraphQL APIs to streamline data fetching in complex applications, significantly reducing the number of requests and improving frontend performance.

Used Cypher to query graph databases, enabling complex relationship-based queries and contributing to projects involving social network analysis and recommendation engines.

Deployed scalable machine learning models and managed big data workflows using Azure services like Data Factory, Synapse, and Databricks, enabling end-to-end automation in enterprise solutions.

Architected data storage solutions with Azure Data Lake, enabling efficient data processing and facilitating advanced analytics across large datasets, improving project outcomes in big data environments.

Built automated ETL pipelines using Azure Data Factory, streamlining data integration across disparate sources and improving the efficiency of data engineering workflows.

Enabled advanced analytics and big data processing with Azure Synapse, enhancing the ability to perform real-time analytics and supporting large-scale data initiatives.

Leveraged Azure Databricks to integrate data science workflows with big data environments, accelerating machine learning model development and deployment.

Utilized Azure DevOps to automate CI/CD pipelines, improving development workflows and enabling faster, more reliable software deployments.

Deployed scalable machine learning models using Azure ML, allowing for rapid iteration and automated model training, testing, and deployment across cloud environments.

Managed and governed data across hybrid environments using Microsoft Purview, improving data compliance and enabling more effective data discovery for business insights.

Developed interactive dashboards and reports in Power BI, providing real-time insights that enabled data-driven decision-making across departments.

Managed version control and collaboration through Git, ensuring consistent codebase management and streamlining development workflows in team-based projects.

Utilized Docker for containerization, enabling consistent application deployment across multiple environments and reducing deployment times.

Created data visualizations in Tableau to deliver actionable insights from complex datasets, improving business intelligence and decision-making processes.

Used TensorFlow to build deep learning models for tasks like image recognition and predictive analytics, enhancing the accuracy of data-driven projects.

Developed neural networks using Keras to optimize machine learning workflows, contributing to more accurate forecasting models in predictive analytics projects.

Applied PyTorch to build flexible deep learning models, accelerating experimentation and optimization in AI projects like NLP and computer vision.

Utilized OpenCV for image processing and computer vision tasks, automating visual data analysis and quality control processes in various applications.

Deployed and managed containerized applications using Kubernetes, automating scaling and load-balancing in production environments, which improved infrastructure resilience and reduced downtime.