Python Backend Developer with Cloud & Microservices Experience

Aman Gautam

+91-770******* ***************@*****.*** LinkedIn GitHub

Education

J.C. Bose University of Science and Technology, YMCA 2017 – 2021 Bachelor of Technology in Computer Science

Experience

Amdocs (Contract) - Software Developer Jul 2025 – Present Gurugram

– Engineered scalable backend services using FastAPI, handling 5K+ daily API requests with asynchronous processing.

– Designed event-driven workflows using Kafka, enabling communication across 3 microservices and reducing service dependency latency by 30%.

– Built APIs publishing 10K+ events/day to Kafka topics for rule validation and downstream processing.

– Optimized SQLAlchemy queries (joins, selectinload), reducing API response time by 35% for high-volume endpoints.

– Monitored and debugged production systems using Splunk, resolving 20+ critical API issues and improving system reliability.

– Implemented secure authentication using JWT and MSAL (Microsoft Authentication Library) token-based authentication for enterprise-grade access control.

– Reviewed 30+ merge requests (MRs) for offshore developers, ensuring code quality, performance optimization, and adherence to backend best practices.

– Actively mentored developers through code reviews, suggesting improvements in API design, database queries, and system scalability.

– Familiarity with AI development tools such as Cursor and GitHub Copilot, leveraging them to enhance development speed and code quality.

Accenture - Software Developer Sept 2021 – Apr 2025 Gurugram

– Developed and maintained 15+ REST APIs using Flask with modular blueprint architecture.

– Resolved 50+ production bugs, improving API stability and reducing client-reported issues.

– Fixed CORS and integration issues, improving frontend-backend communication success rate by 40%.

– Implemented unit and integration tests, increasing code coverage from 40% to 75%. Projects

On-Demand Video Streaming Platform Python, FastAPI, Celery, Redis, FFmpeg

– Built a backend system supporting adaptive video streaming (HLS - .m3u8) serving 100+ concurrent users.

– Implemented asynchronous video processing pipeline using Celery and Redis, reducing processing time by 40%.

– Used FFmpeg to transcode videos into multiple resolutions (240p, 360p, 720p), optimizing bandwidth usage.

– Designed scalable upload system handling large video files up to 500MB efficiently. Technical Skills

Languages: Python

Frameworks: FastAPI, Flask

Databases: PostgreSQL, MongoDB

Messaging & Async: Kafka, Celery, Redis

Tools: Docker, Kubernetes (Basics), Git, Linux, Splunk Concepts: REST APIs, Microservices, Event-Driven Architecture, Asynchronous Processing Practices: Code Reviews, Performance Optimization, System Design Basics

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