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Senior Software Developer

Summary

Lead the design and development of scalable FastAPI-based Python microservices, handling containerization with Docker/Kubernetes, CI/CD pipelines, and monitoring with Prometheus/Grafana.

We are seeking a highly skilled Senior Python Engineer with extensive experience in FastAPI and microservices architecture to join our dynamic team. The ideal candidate will have a strong technical background, proven leadership in technical teams, and expertise in building scalable, resilient, and secure applications.

Key Responsibilities:

• Lead the design, development, and deployment of applications using microservice architecture.

• Develop and maintain FastAPI-based backend services with high performance and scalability.

• Implement best practices in logging, monitoring, health checks, scalability, resilience, service discovery, API gateways, and error handling. • Ensure code quality, security, and performance optimization.

• Work with containerization technologies like Docker and Kubernetes for application deployment.

• Collaborate with cross-functional teams to define, design, and ship new features.

• Establish and manage CI/CD pipelines for seamless application deployment.

• Implement best practices for API design, development, and security.

• Set up and maintain monitoring and logging tools (e.g., Prometheus, Grafana).

• Ensure adherence to version control systems (e.g., Git) and collaborative workflows.



  • Proven experience in leading technical teams and developing applications using microservice architecture.

    • Strong proficiency in Python and FastAPI.

    • Deep understanding of Pydantic for data validation in FastAPI.

    • Experience with containerization (Docker, Kubernetes).

    • Familiarity with CI/CD pipelines and automation tools.

    • Knowledge of API design and implementation best practices.

    • Experience working with monitoring and logging tools (e.g., Prometheus, Grafana).

    • Strong understanding of security best practices in microservices-based applications.

    Nice to Have:

    • Experience with Retriever models, including implementation of chunking strategies.

    • Familiarity with vector databases and their use cases.

    • Understanding of optimal approaches in querying LLM models via API.

    • Experience with prompt engineering and different strategies for effective interactions with LLMs.

    • Exposure to various prompt engineering techniques in different scenarios

See also

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