Python Developer

Introduction:

The candidate is an experienced Senior Backend Developer and Engineering Lead specializing in Python and Django. With a strong background in designing and implementing scalable API services, automation scripts, and AI-driven solutions, they have contributed to multiple enterprise-level projects. Their expertise includes building core financial services, optimizing demand forecasting applications, and developing microservices architectures for large-scale infrastructures. They have successfully led teams to deliver critical backend solutions, including reconciliation, settlement, and dunning flow automation. Their experience spans cloud-based infrastructures like AWS and Azure, with hands-on knowledge of Kubernetes, Docker, Celery, Redis, and ElasticSearch. They have played a key role in real-time synchronization, system migrations, and backend optimizations. With a deep understanding of SQL and NoSQL databases, they have worked with PostgreSQL, MySQL, MSSQL, and DynamoDB to build reliable and high-performance data-driven applications. Additionally, they have contributed to open-source projects and developed web-based platforms for publishing, education, and discussion forums. Their technical stack includes FastAPI, SQLAlchemy, Next.js, React, Grafana, Kibana, and various DevOps tools, ensuring seamless deployment and monitoring of applications. They have also developed frontend widgets and integrated third-party services for various industries.

Responsibilities:

• Design, develop, and maintain scalable API services for enterprise applications.
• Implement RESTful APIs using Python frameworks like Django and FastAPI.
• Optimize existing API services to improve performance, security, and reliability.
• Develop automation scripts for system optimization and process efficiency.
• Ensure real-time data synchronization and smooth integration with external services.
• Design and implement microservices architectures for scalable applications.
• Migrate legacy systems to cloud-based solutions using AWS, Azure, and Kubernetes.
• Manage containerized applications with Docker and Kubernetes.
• Monitor system performance and logs using Grafana, Kibana, and Celery.