Senior Software Engineer

Overview

This role is for one of the Weekday's clients

We are looking for a highly skilled Senior Software Engineer with deep expertise in Python, FastAPI, and microservices architecture. The ideal candidate will have a strong technical background, proven leadership in technical teams, and experience in building scalable, resilient, and secure applications. This role requires hands-on development, architectural decision-making, and collaboration with cross-functional teams to deliver high-performance solutions.

Requirements:
Key Responsibilities::

• Lead the design, development, and deployment of applications using a microservices architecture.
• Develop and maintain FastAPI-based backend services with a focus on performance and scalability.
• Implement best practices for logging, monitoring, health checks, scalability, resilience, service discovery, API gateways, and error handling.
• Ensure code quality, security, and performance optimization.
• Work with containerization technologies such as Docker and Kubernetes for application deployment.
• Collaborate with cross-functional teams to define, design, and deliver new features.
• Establish and manage CI/CD pipelines for efficient 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 microservices architecture.
• Strong proficiency in Python and FastAPI.
• Deep understanding of Pydantic for data validation in FastAPI.
• Experience with containerization technologies (Docker, Kubernetes).
• Familiarity with CI/CD pipelines and automation tools.
• Strong understanding of API design and implementation best practices.
• Hands-on experience with monitoring and logging tools (e.g., Prometheus, Grafana).
• Expertise in security best practices for microservices-based applications.

• Experience with Retriever models, including chunking strategies.
• Familiarity with vector databases and their use cases.
• Understanding of optimal approaches for querying LLM models via API.
• Experience with prompt engineering and strategies for effective interactions with LLMs.
• Exposure to various prompt engineering techniques in different scenarios.