Python Developer Real-Time

Mallikarjun

Mail: ***************@*****.*** Phone: +1 (804) 293- 5516

Senior Full Stack Python Developer

Professional Summary

Drove significant improvements in application scalability and performance as a Python developer with 9 or more years of experience, designing and implementing robust backend systems and enhancing data processing capabilities to efficiently handle high volumes of concurrent users.

Developed scalable backend systems using Python frameworks (Flask, Django), creating RESTful APIs that enabled seamless communication between diverse applications and enhanced interoperability in real-time environments.

Designed and optimized database schemas (MySQL, MongoDB) for efficient data storage and retrieval, implementing indexing strategies that improved query performance and data access.

Created dynamic front-end interfaces using React and Angular, allowing users to interact with backend systems and visualize data in real-time, significantly enhancing user engagement and experience.

Built microservices architectures with Python and Docker, utilizing container orchestration tools like Kubernetes to ensure consistent deployment across development, testing, and production environments.

Leveraged cloud services (AWS, GCP) for hosting Python applications, ensuring scalability, high availability, and compliance with industry standards, including HIPAA and data encryption protocols.

Implemented real-time data streaming solutions using Python with libraries like Kafka and RabbitMQ, enabling efficient processing and immediate insights from IoT devices and other data sources.

Automated CI/CD pipelines using GitHub Actions and CircleCI for Python projects, streamlining the testing and deployment process while maintaining high code quality and accelerating release cycles.

Utilized monitoring and logging tools (Grafana, ELK Stack) to analyze application performance, enabling proactive identification and resolution of potential issues in Python applications.

Developed security measures in Python applications, implementing robust authentication protocols (OpenID Connect) and role-based access control to protect sensitive data and maintain regulatory compliance.

Collaborated with cross-functional teams in an Agile environment, ensuring project requirements were well-defined and solutions aligned with business objectives and user needs.

Conducted user training sessions to educate stakeholders on Python-powered systems, enhancing their ability to effectively utilize new features and functionalities.

Managed Python applications in various environments, including Linux and containerized platforms, ensuring consistent performance and operational stability.

Designed and developed Python-based data processing pipelines for IoT applications, enabling real-time monitoring and predictive analytics for manufacturing processes.

Integrated RESTful APIs in Python to facilitate seamless communication between systems, simplifying the integration of third-party tools for analytics and reporting.

Applied machine learning techniques with Python libraries (like TensorFlow and Scikit-learn) for predictive analytics, enhancing decision-making capabilities in various applications.

Technical Skills:

Programming Languages: Python, Java, C++, Golang, TypeScript, Scala, JavaScript, Ruby.

Web & Frontend Technologies: HTML5, CSS3, jQuery, Bootstrap, React.js, AngularJS, Node.js, Flask.

Backend Frameworks: Django, Flask, Spring, Spring Boot, Ruby on Rails.

Databases: MySQL, PostgreSQL, Oracle, MongoDB, Cassandra, SQLite, HDFS, Hive, Impala.

Cloud Platforms: AWS (EC2, S3), Azure, GCP, Heroku, IBM Cloud.

Data Processing & Big Data: PySpark, Pandas, NumPy, Scikit-learn, Spark, Hadoop, Kafka, RabbitMQ, MapReduce.

DevOps & Containerization: Docker, Kubernetes, Jenkins, Ansible, Terraform, Chef, Git, GitHub, CircleCI.

APIs: RESTful, SOAP, AWS Lambda, Microservices, GraphQL.

Testing & CI/CD: JUnit, PyUnit, TDD, Load Testing, Continuous Integration (CI/CD).

Development Tools & Management: Jira, Confluence, Eclipse, IntelliJ, Toad, WinSCP, Putty.

Operating Systems: Linux (Ubuntu, CentOS), Unix, Windows.

Caching & Asynchronous Tasks: Redis, Apache Ignite, Memcached, Celery, RabbitMQ, Sidekiq.

Machine Learning Frameworks: Scikit-learn, TensorFlow.

Logging, Monitoring & Security: Elasticsearch, Kibana, Grafana, Splunk, Loki, OAuth 2.0, OpenID Connect, JWT, Data encryption techniques, Azure Key Vault.

Design Patterns & Compliance Standards: Observer pattern, Factory pattern, PCI DSS, SEC regulations, HIPAA.

Professional Work Experience:

JPMorgan Chase & Co Washington, DC

Senior Full Stack Python Developer October/2023 – Present

Responsibilities:

Developed scalable backend systems using Python, Django and Golang integrating machine learning models for real-time fraud detection, improving transaction security and accuracy.

Built high-performance APIs using both Django REST Framework and Golang, optimizing communication between backend services and Angular front-end components for better response times.

Built responsive user interfaces with Angular for real-time transaction monitoring and alerts, improving user experience significantly.

Managed PostgreSQL databases for transaction analytics, optimizing queries to efficiently handle high-volume data processing.

Enhanced database interactions with Django ORM, resulting in faster query execution and reduced latency for end-users.

Implemented encryption and tokenization to secure sensitive data, ensuring compliance with financial industry standards and regulations.

Integrated OAuth 2.0 for secure API authentication, establishing role-based access control to safeguard user data and transactions.

Secured user sessions with JSON Web Tokens (JWT), improving security for real-time processing across all APIs and front ends.

Utilized Apache Kafka for event streaming, continuously pushing transaction data to fraud detection models for timely analysis.

Automated fraud detection model retraining and batch processing with Celery, significantly cutting down manual tasks.

Implemented Redis caching for frequently accessed data, reducing backend load and improving application performance.

Employed Elasticsearch and Kibana for real-time logging and monitoring, facilitating quick resolution of system performance issues.

Deployed the backend on AWS EC2, ensuring a fault-tolerant infrastructure capable of scaling with transaction growth.

Used AWS S3 for secure data storage, maintaining compliance through built-in encryption and version control.

Automated alerts and real-time fraud detection tasks using AWS Lambda, enhancing response times and reducing manual oversight.

Containerized the application with Docker, ensuring consistency in development and production environments for efficient deployment.

Managed microservices orchestration with Kubernetes, allowing automatic scaling and optimized resource allocation.

Established CI/CD pipelines in Jenkins to automate testing, building, and deployment processes. Integrated PyTest, UnitTest, MonkeyPatch, and PyTest-Mockito to simulate dependencies and mock external services, ensuring comprehensive test coverage. This automation enhanced the reliability of code releases and significantly reduced manual testing efforts, streamlining the entire delivery cycle.

Developed AI-driven financial automation features that cut manual compliance checks by 40%, enhancing accuracy and operational speed.

Environment: Python (Django, REST Framework, PyTest, UnitTest, MonkeyPatch, PyTest-Mockito), JavaScript (Angular), PostgreSQL, Redis, Elasticsearch, AWS (EC2, S3, Lambda), Docker, Kubernetes, Apache Kafka, Celery, OAuth 2.0, JSON Web Tokens (JWT), Jenkins, Selenium, Grafana, Kibana.

American Express New York, NY

Senior Python Developer August/2021 - October/2023

Responsibilities:

Developed a decentralized asset tokenization system using Python for fractional ownership of physical assets, such as real estate.

Implemented Ethereum smart contracts with web3.py to manage token ownership and transfers, ensuring immutable records.

Replaced IPFS with Swarm for decentralized data storage, utilizing its features for secure metadata storage.

Designed a Django backend for blockchain interactions, managing token creation, transfers, and ownership updates.

Used Django ORM to optimize PostgreSQL interactions for off-chain asset metadata, enhancing query performance.

Created a secure API layer with Django REST Framework to facilitate communication between the blockchain and external systems.

Integrated OAuth 2.0 and JWT for secure user authentication, ensuring access to tokenized assets was restricted to verified owners.

Automated token distribution and dividend payouts through Python-based cron jobs for seamless blockchain transactions.

Utilized Redis caching to improve token transfer and asset query performance, reducing database load.

Managed asynchronous tasks with Celery, improving system efficiency for asset valuation and dividend distribution.

Built monitoring systems with Elasticsearch and Kibana to track blockchain transaction logs and system health.

Integrated Kubernetes for microservices orchestration, optimizing resource management for blockchain services.

Containerized the application with Docker to ensure consistent development and production environments.

Established CI/CD pipelines using GitLab CI to automate testing, building, and deployment for rapid releases.

Integrated real-time blockchain event updates via WebSocket in Django to notify users of token transfers and ownership changes.

Wrote unit and integration tests with Pytest to validate backend and smart contract functionality, covering edge cases.

Deployed on Azure Cloud using Azure Kubernetes Service (AKS) for managed container orchestration.

Stored off-chain data in Azure Blob Storage with encryption to secure sensitive information.

Managed sensitive information with Azure Key Vault to ensure robust security for blockchain transactions.

Improved system performance with optimized Python code, achieving a 35% reduction in transaction processing time.

Designed a graph database (Neo4j) for tracking asset ownership history and visualizing relationships among owners.

Enhanced security using Zero-Knowledge Proofs (ZKPs) for verifying asset ownership while protecting sensitive data.

Automated smart contract audits with Python scripts to ensure compliance with financial regulations.

Created a Python-based analytics dashboard to monitor token price fluctuations and user engagement metrics.

Optimized blockchain communication by integrating gRPC into the Django backend for efficient inter-service communication.

Provided API support for external systems to integrate tokenized asset data for cross-platform transactions.

Achieved improvements in liquidity for tokenized assets, reducing ownership transfer time from 48 hours to 2 hours.

Facilitated fractional ownership of assets, increasing user engagement by 50% through expanded market access.

Environment: Python, Django, Web3.py, Solidity, Ethereum, Swarm, PostgreSQL, Redis, Azure Blob Storage, OAuth 2.0, JWT, Zero-Knowledge Proofs, Azure Key Vault, Celery, Elasticsearch, Kibana, Azure Cloud, AKS, Docker, Kubernetes, GitLab CI, Pytest.

CVS Health Woonsocket, RI

Full Stack Python Developer October/2019 - July/2021

Responsibilities:

Engineered a robust backend framework using Ruby on Rails, facilitating the development of RESTful APIs that enabled seamless communication and data exchange between disparate healthcare systems, ensuring compliance with HL7 and FHIR standards for data interoperability.

Architected a MySQL database schema, optimizing the design for efficient storage and retrieval of complex patient records, while implementing indexing strategies that improved query performance by 30%, crucial for high-volume data operations.

Developed a dynamic Angular frontend, which provided healthcare professionals with an intuitive user interface for accessing, updating, and visualizing patient information in real-time. Utilized Angular Material for responsive design, enhancing the overall user experience across various devices.

Employed Apache Ignite as an in-memory data grid to cache frequently accessed patient data, significantly reducing data access latency by 40% and improving the responsiveness of the application during peak usage periods.

Facilitated real-time data streaming and analytics using Amazon Kinesis, enabling instant updates and alerts on critical patient data changes. This allowed healthcare providers to react promptly to emerging health concerns, improving patient outcomes.

Virtualized application components with LXC (Linux Containers), ensuring consistent environments across development, testing, and production stages. This approach minimized deployment discrepancies and streamlined the onboarding of new team members.

Coordinated microservices management through Apache Mesos, allowing for dynamic resource allocation and scaling of services based on real-time demand. This architecture enabled the platform to handle fluctuating loads effectively.

Leveraged IBM Cloud for hosting the entire application, utilizing its infrastructure to ensure high availability and compliance with healthcare regulations, including HIPAA. Implemented security measures such as data encryption at rest and in transit.

Configured CI/CD pipelines using GitHub Actions, automating testing and deployment processes that reduced deployment times by 50% and ensured rapid iteration cycles without compromising code quality.

Implemented centralized logging and monitoring solutions with Splunk, allowing for the aggregation of logs from multiple services. This enabled proactive monitoring of application health, leading to faster identification and resolution of issues.

Secured user authentication and authorization with OpenID Connect, implementing a robust role-based access control (RBAC) system to ensure that only authorized healthcare personnel could access sensitive patient data, thus maintaining compliance with regulatory standards.

Applied machine learning techniques using TensorFlow for predictive analytics, developing models that analyzed historical patient data to forecast potential health risks. This proactive approach enhanced patient care by facilitating early intervention strategies.

Scheduled and managed background processing tasks with Sidekiq, automating data synchronization processes and report generation. This improved system efficiency and reduced the need for manual interventions, freeing up valuable staff resources.

Conducted comprehensive compliance audits to ensure adherence to HIPAA regulations, reinforcing the platform's commitment to data security and privacy. Established a framework for continuous compliance monitoring and risk assessment.

Collaborated with cross-functional teams, including healthcare providers, data analysts, and regulatory experts, to define project requirements and ensure that the developed solutions aligned with clinical workflows and business objectives.

Facilitated training sessions for end-users, educating healthcare professionals on the new system's features and functionalities, resulting in a smoother transition and increased user adoption rates.

Environment: Ruby on Rails, MySQL, Angular, Angular Material, Apache Ignite, Amazon Kinesis, LXC (Linux Containers), Apache Mesos, IBM Cloud, CI/CD (GitHub Actions), Splunk, OpenID Connect, RBAC, TensorFlow, Sidekiq, HIPAA, data encryption, predictive analytics.

Delta Air Lines Atlanta, GA

Python Developer March/2018 – September/2019

Responsibilities:

Developed an AI-powered chatbot using Python (Flask) for Delta's mobile app to assist customers with inquiries about flight status, baggage tracking, and booking changes.

Created a responsive and interactive user interface using React, allowing customers to engage with the chatbot seamlessly on the Delta mobile platform.

Managed user interaction data using MongoDB, enabling effective storage and retrieval of customer queries and responses for future analysis.

Implemented Memcached as a caching layer to improve response times and reduce database load during peak traffic, enhancing the overall user experience.

Designed RESTful APIs with Flask-Restful to facilitate communication between the chatbot and Delta's backend systems, ensuring efficient data exchange.

Deployed the application on Google Cloud Platform (GCP), providing scalable infrastructure to handle fluctuating user demand while maintaining high availability.

Used Podman for containerization, allowing for easier management and deployment of application instances in a secure and lightweight manner.

Managed the application’s deployment and scaling using OpenShift, providing a robust environment for maintaining application performance.

Utilized RabbitMQ for handling asynchronous tasks, enabling efficient processing of user requests and background operations without delays.

Integrated SpaCy for natural language processing (NLP), improving the chatbot's ability to accurately understand and respond to customer inquiries in real time.

Set up logging and monitoring with Grafana and Loki, enabling tracking of application performance and user interactions to identify potential issues.

Implemented OpenID Connect for secure authentication, providing a reliable method for validating user identities during interactions with the chatbot.

Conducted development in a Linux environment using Ubuntu and CentOS, ensuring consistency and stability throughout the development process.

Established continuous integration and deployment (CI/CD) pipelines using CircleCI, automating the testing and deployment process to maintain code quality and speed up releases.

Integrated D3.js for data visualization, creating interactive dashboards to monitor chatbot performance and customer interaction metrics, facilitating informed decision-making.

Collaborated with cross-functional teams, including UX/UI designers and product managers, to gather requirements and ensure alignment on project goals.

Conducted user testing sessions to gather feedback on chatbot performance and usability, using insights to refine and enhance the chatbot's capabilities.

Environment: Python (Flask), React, MongoDB, Memcached, RESTful APIs, Google Cloud Platform (GCP), Podman, OpenShift, RabbitMQ, SpaCy, Grafana, Loki, OpenID Connect, Linux (Ubuntu, CentOS), CI/CD (CircleCI), D3.js.

AMRITA VISHWA VIDYAPEETHAM GPA - 3.84/4

Computer And Information Sciences June/2011 - MAY/2015

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