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We are looking for a Senior Backend Engineer to join the team. In this role, you will contribute to the development and maintenance of custom-built case management applications used by internal stakeholders. These applications are fully developed and maintained in-house and operate entirely within the Azure cloud environment. You will work within the Back-End chapter as part of a larger multidisciplinary Scrum team consisting of both Backend and Frontend engineers. Although the team is relatively large, work is organised in smaller sub-teams on a quarterly basis to ensure focus, ownership, and effective collaboration.
Job Responsibility:
Design, develop, and maintain scalable backend services and RESTful APIs
Define and maintain clear API contracts and specifications
Build integration components and backend services using Python
Work with frameworks and tooling such as FastAPI, Pydantic, and Pytest
Design and maintain database schemas aligned with application requirements
Write efficient and scalable database queries
Deploy, configure, and operate services within Azure cloud environments
Participate actively in Scrum ceremonies, refinements, and user story discussions
Contribute to code reviews with a coaching and constructive mindset
Collaborate closely with engineers, stakeholders, and other chapters to deliver valuable solutions
Requirements:
Strong experience with Python backend development
Experience designing and developing RESTful APIs
Hands-on experience with FastAPI, Pydantic, and Pytest
Solid understanding of backend architecture and integration patterns
Experience designing and maintaining database schemas
Ability to write efficient and scalable complex queries
Experience with Azure or other cloud platforms
Understanding of deploying and operating backend services in cloud environments
Nice to have:
Experience with AI-assisted development tools and structured AI workflows
Working with tools such as GitHub Copilot
Creating reusable prompts, templates, and repository-level AI instructions
Embedding team standards and architectural decisions into AI-assisted workflows
Critically evaluating AI-generated output for quality, maintainability, and security
Helping scale structured AI usage across engineering teams