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Blend is a premier AI services provider, committed to creating meaningful impact for its clients through the power of data science, AI, technology, and people. We help organisations solve complex business challenges by combining deep domain understanding with modern data and AI capabilities. Our teams work across strategy, analytics, engineering, and product delivery to create scalable, high-value solutions that improve decision-making, efficiency, and growth
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
