AI Native Software Engineer
Remote • Braga, Polo de Negocios de Braga
Posted 5d ago
About the job
We are seeking AI-native engineers who leverage AI as a fundamental part of their workflow, not as an afterthought. In this role, you will design, build, and deploy production-grade software across the full stack, integrating AI-assisted tooling into your daily practice alongside core engineering skills. You will work on client programs in various industries, developing software that connects to and supports agentic AI systems. This involves understanding how your full-stack development integrates with agent architecture, LLM APIs, and enterprise AI pipelines. This is a core engineering function with a direct path to advanced roles for those who develop expertise in agentic systems.
Responsibilities
- Utilize AI coding assistants daily to enhance productivity and output quality.
- Integrate LLM APIs into production applications, managing token limits and latency.
- Apply AI across the software delivery lifecycle, including AI-generated tests, debugging, code review, and prompt engineering.
- Ensure the quality and reliability of AI-generated outputs, exercising engineering judgment on their production readiness.
- Define and track KPIs for AI-assisted workflows and report on AI productivity and quality metrics.
- Manage end-to-end delivery from design to production support within Agile sprint cycles.
- Contribute to shared knowledge bases, reusable components, and internal AI tooling standards.
- Build and integrate application layers, APIs, and interfaces connecting full-stack systems to agentic backends.
- Understand data flows, context handoffs, and integration points between code and AI pipelines.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related field.
- Commercial software engineering experience in production environments or equivalent.
- Proficiency in at least one backend language: Python, Java, or TypeScript.
- Demonstrated hands-on experience using AI tools in daily engineering work, with practical examples.
- Direct experience calling LLM APIs in production code, understanding token management, latency, and cost tradeoffs.
- Basic understanding of web technologies (JavaScript, HTML, CSS).
- Familiarity with cloud fundamentals (AWS, Azure, or GCP), containers (Docker), and CI/CD pipelines.
- Understanding of Agile delivery fundamentals.
- Experience with SQL or NoSQL databases.
- Ability to validate, evaluate, and improve AI-generated outputs, understanding AI limitations and responsible use.
- Familiarity with agentic system concepts, including orchestration frameworks (LangChain, LangGraph, or equivalent) and RAG pipelines.
Benefits
- Vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams.
- Structured AI certification pathways.
- Clear development track toward agentic and forward-deployed engineering.