Hi, I'm Dev Patel

• Implementing .NET, Signal R and C# WebAPIs for system migrations, utilized GRPC for efficient data transfer across microservices.
• Solved bugs across multiple architecture layers and languages(C#,C++, Python,JS, Angular) using dynamic code tracing and memory profiling.
• Crafted REST/MVC requestsfor async tasks on web-responsiveness, SignalR for bi-directional sync and IronPython forscripting simulations

• Architected and deployed Python-based ETL workflows to automate data cleansing, normalization, and integration of multi-source datasets.
• Enhanced disaster management strategies by implementing ML algorithms, focusing on predictive analytics using Pandas, SciPy, and SQL.
• Streamlined data analysis and reporting processes, employing complex SQL queries and data visualization tools for strategic decision-making.

• Developed an e-commerce transaction feature using React and Spring Boot, enhancing UI/UX and server interactions for client experience optimization.
• Integrated PostgreSQL and MongoDB using Hibernate, optimizing data transactions and enhancing query performance for financial analytics.
• Automated data operations with Apache Airflow and Spark, streamlined AWS Glue ETL, improving data management efficiency.
• Implemented AWS S3, EC2, and RDS, ensuring a robust, scalable infrastructure for agile application development and hosting.
• Created predictive models with Scikit-Learn for behavior forecasting, contributing to reliable CI/CD pipelines with Docker and Jenkins.

• Developed a full-stack web application using Express.js and Node.js for server-side processing, integrating React for dynamic client-side rendering.
• Advanced SQL database management and optimization, ensuring efficient data transactions and responsive user interactions.
• Utilized AWS for secure application hosting, integrating client requirements using management tools like JIRA and Git for project tracking.
• Conducted backend performance analytics using Python, ensuring continuous improvement and feature integration in the application.

• Developed web applications using Python, SQL, and React.js.
• Collaborated with cross-functional teams to deliver projects on time.

• Developed predictive models using machine learning algorithms (Decision Trees, Random Forest) for advanced malware threat analysis.
• Implemented a React-based analytics dashboard for visualizing data extracted from malware samples using Python libraries.
• Conducted malware code analysis employing reverse engineering with Ghidra and feature extraction algorithms for detection accuracy.
• Designed and tested various machine learning-based malware detection methods, focusing on precision and recall metrics.

• Assisted in technical maintenance tasks, ensuring smooth functioning of computer systems and conducted regular system checks, troubleshooting hardware and software issues to minimize downtime and maximize productivity.
• Utilized Python programming language to automate Excel data management processes, such as organizing and manipulating spreadsheets, performing data analysis, and generating reports for departmental use.
• Managed Excel data by organizing and maintaining spreadsheets, performing data entry, and generating reports for departmental use.
• Actively participated in team meetings and contributed ideas for optimizing IT support services across the department.

• Managed databases to support the production of 4-chloro, 2-Aminophenol chemicals, ensuring accurate and efficient access to critical data. Utilized mathematical, chemical, and computer knowledge to optimize productivity levels and streamline production processes.
• Analyzed chemical data and modeled experiments using MySQL and Python (including libraries such as NumPy, Matplotlib, and Stats) to improve reaction rates for the production of the compound.
• Utilized MySQL to create and manage databases, ensuring efficient data storage, retrieval, and analysis.
• Leveraged Python programming and data analysis libraries to process and visualize chemical data, facilitating informed decision-making and process optimization.
• Generated reports and visual representations of experimental data, presenting findings to the research team and contributing to project discussions.