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Aditya C - Python Developer

[email protected]

Location: Hoboken, New Jersey, USA

Relocation: Yes

Visa: GC-EAD

Aditya C E-Mail: [email protected] Ph: +1 (916) 877-5218 Python Developer SUMMARY: I have over 9 years of experience in Python, specializing in backend efficiency, scalable system design, and performance optimization. Proficient in developing dynamic, high-performance web applications and APIs with a focus on automation, cloud integration, and data-driven solutions. Developed and scaled web applications with Django, Flask, and Pyramid. Built and maintained RESTful APIs and microservices, focusing on reliability, performance, and security to enhance modularity and scalability. Designed and developed interactive web applications using Django, integrating with React frontends and optimizing backend performance. Created reusable Django components, implemented real-time data updates, and enhanced frontend features using HTML5, CSS3, and Bootstrap. Built interactive dashboards and visualizations with Django, Tableau, Power BI, and AWS QuickSight, improving data-driven decision-making. Utilized AWS Lambda and Azure Functions for automation, PySpark and Scala for data analysis, and Scikit-learn and TensorFlow for machine learning. Managed cloud databases, deployed applications with Kubernetes, and implemented CI/CD pipelines using Jenkins and GitLab CI/CD. Ensured seamless integration and deployment of applications in dynamic environments. Developed and optimized Django applications with a focus on performance and scalability. Integrated AWS services (EC2, Lambda, Athena) and automated workflows using AWS Glue and Step Functions. Managed databases (MongoDB, DB2, Sybase) and deployed microservices with Kubernetes. Developed scalable chatbot systems using Flask, integrating with AI frameworks (Rasa, Dialogflow) for customer service automation. Created and maintained RESTful APIs, optimized frontend with AngularJS, and managed AWS infrastructure (EC2, S3, Lambda). Automated workflows and deployments using DevOps tools like Jenkins, Docker, and Kubernetes. Managed databases and created interactive dashboards with Tableau and AWS QuickSight. Extensive experience in data analysis and transformation using Pandas, NumPy, SciPy, PySpark, and AWS Glue. Designed and optimized ETL workflows for large-scale data processing, leveraging Snowflake and Amazon Redshift for advanced data warehousing solutions. Applied PySpark to build scalable data transformation pipelines, enhancing processing efficiency for large datasets. Deep expertise with AWS services (EC2, S3, Lambda, RDS, Redshift, CloudWatch, VPC, Kinesis, ECS, EMR), Azure, and Google Cloud Platform (GCP). Developed serverless architectures using AWS Lambda, improving performance and reducing infrastructure costs. Developed and maintained Python scripts for automation, integrating REST APIs and cloud services for seamless data workflows. Utilized Requests for efficient HTTP operations and Jinja2 for flexible HTML templating in automation tasks. Developed and fine-tuned machine learning models using Scikit-learn, TensorFlow, and Keras for predictive analytics, data classification, and model optimization. Built models to drive insights and data-driven decision-making in large-scale systems. Visualized data insights using Matplotlib, Seaborn, and Tableau. Proficient in CI/CD pipelines using Jenkins, GitLab CI/CD, and CircleCI, automating build, test, and deployment processes for reliable and fast delivery. Automated cloud infrastructure provisioning and management across AWS, Azure, and GCP using Terraform, improving deployment speed and consistency while reducing manual configuration errors by 40%. Streamlined server provisioning and application deployments using Ansible, achieving a 30% reduction in deployment time and increasing operational efficiency through automation and modularized configurations. Deployed containerized applications with Docker and managed orchestration using Kubernetes and Azure Kubernetes Service (AKS). Experienced in Docker, Kubernetes, and configuration management with Ansible, Puppet, and Chef. Managed and optimized relational and NoSQL databases (MySQL, PostgreSQL, Oracle, MongoDB, Redis, Cassandra). Developed and tuned data warehousing solutions with Snowflake, Amazon Redshift, and SQL Server. Utilized Django ORM and SQLAlchemy for efficient CRUD operations and complex queries. Developed responsive, interactive web applications using ReactJS, HTML5, CSS3, and Bootstrap. Integrated dynamic content updates using ReactJS hooks and state management, improving user experience and enhancing front-end performance. Ensured cross-device compatibility with advanced CSS techniques and responsive design principles. Designed and secured cloud environments across AWS, GCP, and Azure. Configured secure connectivity using Azure ExpressRoute, Azure VPN, and VPC for private and hybrid cloud deployments, ensuring compliance with security standards. Integrated third-party messaging services like RabbitMQ for efficient, asynchronous communication between system components. Automated workflows using serverless solutions, ensuring real-time processing and optimization of system workflows. TECHNICAL SKILLS: Programming & Scripting Languages Python (2.7, 3.7), C, C++, SQL, Shell Scripting, R Web Development Django, Flask, Pyramid, FastAPI, ReactJS, Redux, AngularJS, JavaScript, TypeScript, HTML5, CSS3, Bootstrap, jQuery, AJAX, Jinja2, Beautiful Soup, DOM, SAX, httplib2 Machine Learning & Data Science NumPy, SciPy, Scikit-learn, TensorFlow, Keras, PySpark, RASA, wxPython, PyTables, PySide, Matplotlib, Seaborn Data Analytics & Visualization Tableau, Power BI, AWS QuickSight, SSRS, MS Excel Big Data & Distributed Computing PySpark, Hadoop, RabbitMQ Protocols TCP/IP, HTTP/HTTPS, SNMP, SMTP Databases MySQL, SQL Server, Teradata, Oracle, PostgreSQL, DB2, Sybase/Sybase IQ, MongoDB, Redis, Cassandra, AWS Redshift, Snowflake. Cloud Computing AWS (EC2, ELB, VPC, RDS, AMI, IAM, CloudFormation, S3, Lambda, Aurora), Azure (VPN, Virtual Networks, ExpressRoute, Azure Kubernetes Service, Azure Data Storage, Azure Resource Manager), GCP (Google App Engine, BigQuery) CI/CD Tools Heroku, Jenkins, CircleCI, GitHub Actions. Containerization & Orchestration Docker, Kubernetes, Azure Container Services Configuration Management & Automation Terraform, Ansible Testing PyTest, Selenium, Cypress, Jest, Mocha, unittest, Types(Unit Testing, Integration Testing, End-to-End Testing) IDEs NetBeans, PyCharm, Android Studio, Eclipse, Sublime Text, Visual Studio Version Control Git (GitHub, Bitbucket), SVN Tracking Tools JIRA, Bugzilla, Redmine Methodologies Agile, Scrum, and Waterfall Operating systems Linux/Unix, Windows Variants PROFESSIONAL EXPERIENCE: Client: Abbott, NY. Jan 2022 Present Python Developer Responsibilities: Designed and developed interactive and responsive web applications using Django, focusing on delivering a dynamic user experience with real-time data updates and seamless integration with frontend services. Employed Django s state management solutions and middleware to ensure application consistency, high performance, and efficient data handling. Created and optimized reusable Django components and custom middleware, enhancing development efficiency and application maintainability through modularity and code reuse. Integrated Django backends with React frontends via RESTful APIs, ensuring seamless data flow and robust user interactions while adhering to best practices for API design and security. Developed and optimized backend interfaces using Django, HTML5, CSS3, and Bootstrap. Implemented dynamic content updates and interactive features using Django s templating system and reactive programming capabilities, improving frontend performance and user engagement. Created interactive dashboards and complex visualizations using Django, Tableau, Power BI, and AWS QuickSight, enabling data-driven decision-making and strategic insights for business and clinical contexts. Improved backend performance with Django, achieving a 30% reduction in response times and a 25% increase in system throughput through optimized query handling and efficient data processing. Developed Django-based automation scripts, integrating with AWS Lambda and Azure Functions to streamline processes and handle data processing effectively. Utilized Pandas, PySpark, and Scala for data manipulation and analysis within Django applications, managing datasets over 10 TB and reducing processing time by 35% with AWS Redshift and Azure Data Lake. Applied machine learning models using Scikit-learn, TensorFlow, and Keras within Django applications, enhancing predictive accuracy by 25% and reducing model training time by 50%. Utilized AWS Lambda for triggering automated workflows within Django applications, enabling real-time data processing and reducing latency. Managed and optimized cloud-based databases with Django, including AWS RDS, ensuring high availability and data integrity for critical applications. Deployed Django applications in containerized environments using Kubernetes, streamlining the scaling and management of microservices. Leveraged AWS Aurora with Django for improved database performance, reducing query execution time in high-traffic scenarios. Migrated Django-based infrastructure to serverless architectures with AWS Lambda, reducing operational overhead by 45% and scaling services for a 60% traffic increase. Managed containerized environments for Django with Docker and Kubernetes, and automated CI/CD pipelines using Jenkins, GitLab CI/CD, and Terraform for infrastructure management. Implemented Infrastructure as Code (IaC) using Terraform and Ansible, automating provisioning, configuration, and scaling of cloud resources, and streamlining deployment processes. Automated configuration management using Ansible, improving system reliability, and reducing manual intervention by over 40%, ensuring consistent environments across development, testing, and production. Integrated MongoDB, Cassandra, and Redis with Django for flexible, high-performance data storage and caching. Managed and optimized databases including PostgreSQL, MySQL, Snowflake, Oracle, and SQL Server, handling schema design, data migration, and performance tuning. Implemented unit and integration tests for Django services using PyTest, ensuring code quality and application reliability. Conducted end-to-end testing with Cypress and Selenium to validate Django applications functionality and performance. Employed test-driven development (TDD) practices to catch issues early and maintain high code quality. Applied Agile methodologies for iterative development, using JIRA and Redmine for project tracking and team collaboration. Ensured timely delivery of features and adaptability to changing requirements. Integrated third-party services and tools with Django to enhance system functionality and automate workflows, improving overall operational efficiency and effectiveness. Environment: Python (3.7, 3.8, 3.9), Django, Django REST framework, ReactJS, JavaScript, HTML5, CSS3, Bootstrap, AWS (S3, Redshift, Glue, Lambda, CloudFormation), Azure (Data Storage, Kubernetes Service), Terraform, Ansible, Pandas, NumPy, Tableau, Power BI, PySpark, Scala, Hadoop, Scikit-learn, TensorFlow, Keras, Matplotlib, Seaborn, MongoDB, Cassandra, Redis, GitHub, Docker, Kubernetes, Jenkins, GitLab CI/CD, JIRA, Redmine, CircleCI, Linux, Shell Scripting, PyTest, Cypress, Selenium. Client: Fifth Third Bank, IL. Oct 2019 Dec 2021 Python Developer Responsibilities: Designed and implemented high-performance applications using Python and Django, focusing on efficient data handling, performance optimization, and robust architecture. Adhered to best practices for code quality and maintainability. Developed modular and scalable Django applications using Django's component-based architecture and dependency injection principles. Improved application structure and flexibility through effective use of Django's built-in features. Employed Pandas for detailed statistical analysis within Django applications and used PySpark for processing large datasets. Facilitated efficient data manipulation and extraction of actionable insights, supporting business intelligence. Integrated AWS services such as EC2, Lambda, and Athena with Django applications. Utilized AWS Lambda for serverless computing, EC2 for scalable virtual servers, and Athena for interactive querying of data in S3. Automated backend processing tasks by integrating AWS Lambda with Django applications, reducing operational overhead and streamlining workflows. Deployed Django-based microservices using Kubernetes, enhancing scalability and load balancing across the infrastructure. Managed relational databases using Django ORM and AWS RDS, and distributed storage systems including MongoDB, DB2, and Sybase/Sybase IQ. Ensured efficient data storage, retrieval, and performance optimization for mission-critical services. Managed infrastructure using Terraform, Ansible, Docker, and Kubernetes. Automated infrastructure provisioning and configuration management using Terraform and Ansible, while Docker and Kubernetes were utilized for containerization and orchestration, ensuring efficient deployment and scaling of Django applications. Automated CI/CD pipelines for Django applications with Jenkins, CircleCI, and Terraform, streamlining deployment processes and enhancing development workflows. Implemented continuous integration and deployment practices for rapid and reliable feature delivery. Automated data loading into SQL databases with Django using AWS Glue and Step Functions, optimizing ETL workflows. Designed efficient data pipelines to handle large volumes of data and ensure timely processing. Developed machine learning models with Scikit-learn and TensorFlow within Django applications. Created predictive models to support data-driven decision-making, using GCP for data processing and developing recommendation systems. Designed BI solutions with Django integration, using Tableau and AWS QuickSight to create interactive dashboards and reports. Developed complex visualizations for strategic planning and operational improvements. Built and maintained backend components with Django and Flask. Developed RESTful APIs and backend services to support frontend applications and ensure seamless data integration. Enhanced user interfaces with Django templating and integration with ReactJS, HTML5, CSS3, and Bootstrap. Applied modern frontend technologies and frameworks to build interactive and dynamic web applications. Managed and optimized databases including MySQL, PostgreSQL, AWS Redshift, Snowflake, and MongoDB within Django projects. Handled schema design, data migrations, performance tuning, and implemented best practices for data integrity and efficient querying. Embraced Agile methodologies for iterative development. Participated in Agile sprints, daily stand-ups, and retrospectives to adapt to changing requirements and enhance project outcomes. Developed automated testing tools and scripts for Django, including unit tests, integration tests, and end-to-end tests. Used testing frameworks to validate functionality and performance, ensuring high-quality code delivery. Utilized JIRA for project management and tracking. Facilitated effective team collaboration, progress monitoring, and issue resolution. Managed project workflows and ensured alignment with project goals and deadlines. Integrated various third-party services and tools with Django to enhance system functionality and automate workflows. Implemented solutions to streamline processes, improve efficiency, and support diverse application requirements. Environment: Python (2.7, 3.7), Django, Django REST framework, Pandas, PySpark, Scala, AWS (EC2, Lambda, Athena, S3, Glue, Redshift), Azure (Data Storage, Virtual Networks), Google Cloud Platform (GCP), Scikit-learn, TensorFlow, Tableau, AWS QuickSight, ReactJS, Redux, JavaScript, HTML5, CSS3, Bootstrap, JQuery, AJAX, Docker, Kubernetes, Ansible, Terraform, Jenkins, CircleCI, GitHub, SVN, JIRA, Linux, Shell Scripting, MongoDB, DB2, Sybase/Sybase IQ. Client: T-Mobile, TN. Oct 2017 Sept 2019 Python Developer Responsibilities: Designed and developed scalable chatbot-driven data management systems using Python and Flask, focusing on customer service automation for troubleshooting, billing inquiries, and plan upgrades. Applied advanced database design principles and optimization techniques to ensure high performance, scalability, and reliability across chatbot interactions. Developed conversational AI using Flask with frameworks like Rasa and Dialogflow, integrating AI-driven chatbots with T-Mobile s customer service platform to automate common support queries and provide real-time solutions. Created and maintained Python scripts for automation, including the development of RESTful APIs with Flask to facilitate seamless integration between chatbots, backend CRM systems, user data, and billing platforms. Automated routine tasks to enhance operational efficiency and minimize manual intervention. Developed backend modules using Flask, focusing on RESTful APIs and microservices to support chatbot integration, scalable architectures, and real-time processing of customer data. Optimized chatbot frontend components with AngularJS, HTML5, CSS3, and Bootstrap, creating responsive, user-friendly interfaces for customers to engage with chatbots for both troubleshooting and promotional offers. Applied modern front-end technologies to enhance user experience and ensure cross-browser compatibility. Utilized Pandas for detailed data analysis and manipulation, performing ETL tasks and managing chatbot interaction data to drive insights into customer behavior and optimize response accuracy. Applied Scikit-learn for developing machine learning models to enhance chatbot performance in predictive analytics, customer intent recognition, and data classification, improving chatbot accuracy and customer engagement. Configured and managed AWS cloud infrastructure (EC2, S3, VPC, RDS, CloudWatch) for chatbot deployment, ensuring high availability, security, and performance for mission-critical customer support services. Implemented serverless architectures using AWS Lambda to manage chatbot interactions at scale and reduce operational overhead. Automated chatbot infrastructure deployments with AWS CloudFormation, enabling Infrastructure as Code (IaC) practices. Automated infrastructure and application deployments using DevOps tools like Jenkins, CircleCI, Docker, and Kubernetes, streamlining CI/CD pipelines for chatbot updates and ensuring efficient feature delivery. Managed and optimized databases including MySQL, MongoDB, PostgreSQL, Snowflake, and distributed systems like DB2 and Sybase/Sybase IQ, handling chatbot-related schema design, data migrations, complex queries, and performance tuning. Ensured robust data management to support high-traffic interactions. Leveraged Flask-SQLAlchemy for efficient database interactions within chatbot services, simplifying data access and manipulation, and ensuring code maintainability. Integrated messaging services like RabbitMQ to manage real-time communication between chatbots and backend systems. Leveraged AWS Lambda for serverless functions to streamline processes across customer support workflows. Developed automated testing frameworks and solutions for unit, integration, and regression testing of Flask-based chatbot services, ensuring high-quality software delivery and accuracy of customer interactions. Created and maintained interactive dashboards and reports using BI tools like Tableau and AWS QuickSight, visualizing chatbot usage metrics and delivering actionable insights for optimizing user engagement, sales, and technical support. Environment: Python (2.7, 3.7), Flask, Rasa, Dialogflow, AngularJS, HTML5, CSS3, Bootstrap, JavaScript, Pandas, NumPy, Scikit-learn, TensorFlow, MySQL, MongoDB, PostgreSQL, Snowflake, DB2, Sybase/Sybase IQ, AWS (EC2, S3, VPC, RDS, CloudWatch, Lambda, CloudFormation), Azure, RabbitMQ, Docker, Kubernetes, Jenkins, CircleCI, Git, SVN, PyCharm, Eclipse IDE Python Developer Responsibilities: Developed Flask-based microservices architecture for scalable and modular applications, separating concerns into smaller, more manageable services, which improved maintainability and system scalability. Implemented Redis as an in-memory data store for caching frequently accessed data, reducing response times for high-traffic endpoints and optimizing performance. Integrated Flask applications with external APIs, managing authentication, rate-limiting, and error handling to ensure smooth interaction with third-party services and APIs. Enhanced security features by implementing OAuth 2.0 and improving data encryption mechanisms in Flask applications, ensuring secure user data transmission and API access control. Used SQLAlchemy to optimize database interactions, including lazy loading, connection pooling, and batch processing techniques, leading to a reduction in database query time and overall performance enhancement. Deployed applications on cloud platforms (AWS and Azure) using CI/CD pipelines. Implemented auto-scaling, load balancing, and disaster recovery strategies to ensure high availability and fault tolerance. Wrote unit tests for AngularJS front-end components using Jasmine and Karma, ensuring that UI elements and services behave as expected in various scenarios. Integrated Flask applications with Celery for background task processing, enabling asynchronous execution of time-consuming tasks like data uploads, email notifications, and API calls. Designed and implemented Flask middleware components, allowing customization of request handling, logging, and security enhancements. Leveraged Flask blueprints to modularize the application and improve code organization, promoting code reuse and reducing development time for large-scale projects. Developed ETL pipelines for data integration between different sources and Snowflake using Python and Pandas, automating data cleaning and transformation processes for analytical reporting. Used Docker for local development and production deployment, ensuring consistent environments across different stages of the SDLC (software development life cycle). Implemented rate-limiting mechanisms in Flask APIs to prevent abuse and ensure system stability under heavy load, enhancing the security and resilience of the application. Integrated Flask applications with Elasticsearch, enabling full-text search capabilities across large datasets, improving data accessibility and retrieval speed for end-users like Docker and Heroku, streamlining application delivery and ensuring consistent deployment environments. Reduced API response times by 40% through performance optimizations using Redis caching, Flask-SQLAlchemy improvements, and query optimizations. Improved data processing speed by 30% by integrating Celery for asynchronous task execution, handling large data uploads and time-consuming tasks without blocking the main application thread. Enhanced security by integrating OAuth 2.0 and JWT for API authentication and authorization, safeguarding user data and access control. Environment: Flask, Python, AngularJS, JavaScript (ES6+), HTML5, CSS3, Bootstrap, PostgreSQL, MySQL, MongoDB, Redis, Pandas, NumPy, SQLAlchemy, Celery, Jasmine, Karma, OAuth 2.0, JWT, Docker, Heroku, Jenkins, Snowflake, Tableau, AWS QuickSight, Elasticsearch, AWS (EC2, S3, Lambda, RDS), Azure (VMs, Blob Storage), Git, Bitbucket, Redmine. Keywords: cprogramm cplusplus continuous integration continuous deployment artificial intelligence user interface business intelligence sthree rlang trade national microsoft Illinois New York Tennessee