Engenheiro de Software AI-Native (Dominio em Python)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Engenheiro de Software AI-Native (Domínio em Python) based in Brazil.

This role offers the opportunity to build software using an AI-native engineering approach, combining strong Python expertise with generative AI and agentic technologies.

You will help transform business challenges into scalable digital products that can impact thousands of users across a large organization.

The position sits at the intersection of software engineering, AI orchestration, automation, and product thinking.

You will work with LLMs and AI agents while maintaining full technical ownership of the solutions delivered.

A key part of the role is turning requirements into precise specifications, reusable contexts, testable plans, and reliable software.

You will operate in a collaborative, agile environment where experimentation, critical thinking, and continuous learning are highly valued.

This is an ideal opportunity for a strong Python engineer who wants to help shape how AI-native software is designed, built, tested, and operated.

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.