Engenheiro de Software AI-Native (Dominio em Python)

Accountabilities:

• • Develop software using an AI-native approach, orchestrating agents and LLMs from clear specifications while maintaining full technical responsibility for the final outcome.
  • Lead Spec-Driven Development, translating business and technical requirements into executable specifications, implementation plans, and clear acceptance criteria.
  • Manage progressive AI autonomy according to risk and context maturity, determining when autonomous generation is appropriate and when human intervention or deeper validation is required.
  • Participate in augmented Pull Request reviews, evaluating not only syntax and implementation but also intent, acceptance criteria, architectural decisions, functional impact, and AI-generated code.
  • Build and maintain reusable knowledge and context assets, including skills, engineering patterns, architectural decisions, connectors, and MCP integrations.
  • Apply sound software architecture and engineering practices to deliver clean, maintainable, testable solutions across monolithic and distributed systems.
  • Develop automated tests and observability practices, including mechanisms to monitor the quality, reliability, and cost of AI-generated outputs.
  • Identify opportunities to reduce rework and technical debt while maintaining a sustainable development pace.
  • Collaborate with Product, Engineering, and other multidisciplinary teams through agile practices such as code reviews, pair programming, and mob programming.
  • Share knowledge, contribute to technical discussions, and continuously improve the team's AI-native engineering practices.

  Requirements:

  • At least 3 years of professional software development experience, with strong proficiency in Python and the ability to work across other technologies with support from AI tools. Full-stack experience is valued.
  • Strong understanding of Spec-Driven Development, including the ability to write unambiguous specifications, break requirements into verifiable plans, and establish acceptance criteria for both humans and AI agents.
  • Experience with context engineering for LLMs, including instructions, constraints, examples, tool/MCP selection, and context-window management.
  • Practical experience using generative AI and agentic development tools, with sound judgment, critical thinking, and systematic human validation.
  • Ability to critically review and improve code written by AI or other developers with the same rigor applied to personally authored code.
  • Strong programming fundamentals and ability to produce clean, organized, maintainable, and testable code using object-oriented programming and/or sound software design principles.
  • Experience with automated testing, including unit and/or integration testing.
  • Proficiency with Git and collaborative development through Pull Requests.
  • Experience with SQL databases and fundamental data modeling concepts.
  • Familiarity with agile methodologies such as Scrum, Kanban, or XP.
  • Strong written communication and specification skills, with the ability to clearly define requirements, technical decisions, and trade-offs.
  • Critical thinking and healthy skepticism toward automated solutions, particularly AI-generated outputs.
  • Autonomy, ownership, adaptability, and comfort operating in ambiguous and rapidly changing environments.
  • Collaborative mindset, strong communication skills, and genuine interest in the success of both the product and the team.

  Nice to have:

  • Practical experience with AI agent orchestration, MCPs, and connectors, including tool composition and AI-driven workflow automation.
  • Experience evaluating and observing AI outputs, including quality measurement, regression detection, and token-cost monitoring.
  • Knowledge of model-agnostic architectures and AI evaluation practices.
  • Experience with distributed architectures, microservices, messaging, asynchronous or parallel processing, and queues.
  • Experience with CI/CD, automated builds, and release processes.
  • Knowledge of cloud and container technologies such as Docker, Kubernetes, GCP, or Azure DevOps.
  • Experience with GenAI frameworks such as LlamaIndex or LangChain, as well as RAG, Tools, and MCP development.
  • Experience with Streamlit, Pandas, or low-code automation platforms such as n8n.
  • Experience automating processes involving SAP HANA.
  • Familiarity with data-intensive applications and scalable AI-driven solutions.

  Benefits:

  • Remote work, offering flexibility and autonomy in your working environment.
  • Opportunity to work on AI-native software development and emerging generative AI technologies.
  • Exposure to advanced practices involving LLMs, AI agents, MCPs, automation, and context engineering.
  • Collaborative environment focused on continuous learning, knowledge sharing, and technical growth.
  • Opportunities to work on solutions with significant business impact and large-scale adoption.
  • Participation in an innovative technology ecosystem with international exposure.
  • Professional development initiatives, communities, and learning opportunities designed to support continuous growth.
  • Inclusive culture that values diversity, respect, ethics, autonomy, and collaboration.