Develop and evolve backend services in Python, building APIs and business components with a focus on quality, readability, and maintainability.
Contribute to the development of digital solutions by applying software architecture and engineering best practices (Clean Architecture, SOLID, modeling and design patterns), using them pragmatically in day-to-day work.
Contribute to projects that use agile methodologies and practices such as code review, pair/mob programming, continuous integration and quality automation to deliver high-quality software.
Work in an AI-augmented development cycle: use generative AI (e.g., Copilot and LLMs) to accelerate coding, documentation and analysis, while maintaining a critical approach and performing technical validation of results.
Contribute to Spec-Driven Development, helping to turn requirements into clear specifications and contextual artifacts (e.g., /specify, /plan...) that guide AI agents and reduce ambiguities.
Take part in reviews focused on “intentionality”: beyond syntax, verify adherence to acceptance criteria, consistency with technical decisions, and the functional impact of code (including when generated by AI).
Collaborate on DevOps/CI/CD and observability practices according to the team context, aiming for frequent and reliable deliveries.
Support the maintenance and continuous evolution of the product, contributing to bug fixes, improvements, and sustainable reduction of technical debt.

Practical (Senior-level) experience in backend development with Python (e.g., FastAPI, Django or Flask).
Experience with databases (SQL and NoSQL) and data modeling/consumption in applications.
Engineering best practices: object-oriented programming, code organization, Git versioning and collaboration via Pull Requests.
Experience with automated testing (unit and/or integration tests).
Familiarity with agile methodologies (Scrum, Kanban, XP) and collaboration within multidisciplinary teams.
Familiarity with Spec-Driven tools (e.g., specify-cli, GitHub Spec Kit) and with organizing technical context.
Knowledge of Prompt Engineering for LLMs and the ability to write instructions/contexts/constraints that reduce ambiguities.
Experience with CI/CD, Docker and Kubernetes.
Nice to have: experience with microservices, messaging systems (RabbitMQ/Kafka) and/or event-driven architectures.
Experience with TDD/BDD and validation techniques for AI-assisted generated code.
Knowledge of MCP (Model Context Protocol) for integrating tools and external sources into the AI context.
Experience with observability (logs, metrics, tracing) and reliability best practices.
Experience with Autodesk APIs (APS / Forge / Revit API).
Experience with Revit (data structure and parameters) and Autodesk Construction Cloud (ACC).

Software Developer

Key skills