Python Backend Engineer

Job Description:

Key Responsibilities: 

Large-Scale System Design: 

Architect and design robust, scalable, and resilient backend systems capable of handling high volumes of traffic. 

Implement best practices for system design, focusing on performance, scalability, and fault tolerance. 

API Gateway Management: 

Design and implement APIs, working with API Gateway (API GW) for scalable and secure communication. 

Establish throttling, failures, and retry strategies for APIs to handle traffic spikes, load balancing, and graceful failure handling. 

Serverless & AWS Lambda: 

Develop and deploy serverless functions using AWS Lambda to create highly scalable and cost-effective solutions. 

Optimize serverless applications for performance and resilience, ensuring they can handle real-time data and traffic. 

Throttling, Failures & Retry Strategies: 

Design strategies to manage API throttling limits, ensuring availability and reliability of services. 

Implement retry and failure handling mechanisms to improve the system’s ability to recover from transient errors or failures. 

Semantic Cache Management: 

Design and implement semantic caching strategies to improve the speed and efficiency of data retrieval. 

Ensure optimal cache management policies, avoiding stale data and ensuring timely updates across the system. 

Required Qualifications: 

Programming Languages: 

Strong proficiency in NodeJS and Python. 

Experience with backend frameworks such as Express, FastAPI, Flask, etc. 

API & System Design: 

Experience with designing and scaling RESTful APIs and GraphQL. 

Strong understanding of API Gateway concepts and implementations. 

Serverless Computing: 

Hands-on experience with AWS Lambda and other serverless technologies. 

Failure Handling & Retry Strategies: 

Experience in designing and implementing throttling, failure recovery, and retry strategies for large-scale systems. 

Caching Strategies: 

Knowledge of semantic cache management techniques and caching tools such as Redis or Memcached. 

Cloud Infrastructure: 

Strong experience with cloud platforms (preferably AWS) and distributed systems architecture. 

Preferred Qualifications: 

Experience with monitoring and logging tools like CloudWatch, Datadog, or Prometheus. 

Familiarity with containerization (Docker, Kubernetes) and infrastructure as code (Terraform, CloudFormation). 

Strong understanding of microservices architecture and event-driven systems