Ai Platform Engineer - Syone Lisbon (Hybrid), Porto (Hybrid)

Act Digital; Viseu; Híbrido; EUR 90 000 - 120 000; Tempo integral

Resumo da oferta

A Act Digital está recrutando um Senior AI Platform Engineer para uma equipe DevEx, criando o ecossistema de desenvolvimento orientado por IA. Você planeja, constrói e valida frameworks, agentes e integrações que permitem que equipes de engenharia trabalhem com IA com segurança e eficiência.

Este papel envolve liderança técnica, PoCs de IA-nativa, e a concepção de ferramentas de linha de comando, plataformas internas e automação para acelerar o desenvolvimento de software.

Qualificações

• 5+ anos de experiência em engenharia de software, preferencialmente Backend/DevOps/Platform.
• Pelo menos 2 anos com sistemas AI/Agentic e integração de IA em fluxos de desenvolvimento.

Responsabilidades

• Arquitetar e implementar fluxos sofisticados com agentes IA e multiagentes.
• Desenhar ferramentas reutilizáveis que automatizam etapas do ciclo de vida de software.
• Integrar com MCP e ferramentas internas, mantendo contexto e governança.
• Contribuir para a plataforma interna de developers, CLI e tooling.

Conhecimentos

Software Engineering

IA/Agentic systems

LLM orchestration

RAG architectures

MCP integration

DevEx focus

Descrição da oferta de emprego

Descrição do cargo

Are you passionate about AI-native engineering, developer productivity, and building platforms that change how software teams work?

We’re looking for a Senior AI Platform Engineer to join a Developer Experience (DevEx) team and become a founding member of a specialized Engineering Intelligence function. This is a highly technical and strategic role where you’ll help build the foundations for AI-native software development workflows used across a large R&D organization. Rather than simply consuming AI tools, you’ll design the frameworks, agents, integrations, commands, and abstractions that enable engineering teams to work more effectively with AI.

What You’ll Be Doing:

• AI Agents & Orchestration Architect and implement sophisticated AI-native and agentic workflows. Design and orchestrate multi-agent systems. Build codebase-specific RAG (Retrieval-Augmented Generation) solutions. Develop evaluation frameworks and feedback loops to continuously improve the quality and reliability of AI-assisted outputs. Explore and validate new approaches through technical PoCs and experimentation.
• Developer Workflows & Automation: Design reusable Skills, Agents, and Commands that automate key stages of the software development lifecycle. Transform complex manual engineering processes into scalable, AI-assisted workflows. Build abstractions that can be easily adopted and extended by other engineering teams. Optimize the developer inner loop, from specification and planning through implementation and validation.
• MCP & Ecosystem Integration: Work extensively with Model Context Protocol (MCP) and related integration patterns. Connect AI agents with internal engineering tools, including version control, task management, documentation, and development platforms. Ensure agents have access to accurate, high-quality context while maintaining appropriate security and governance.
• Platform, CLI & Developer Tooling: Contribute to the evolution of an internal developer platform/marketplace and multi-target CLI. Build scalable developer-facing tools, commands, plugins, and integrations. Ensure the platform remains secure, governed, maintainable, and extensible.
• Create foundations that enable other engineering teams to build their own AI capabilities. Engineering Intelligence: Define and implement telemetry to understand and improve engineering effectiveness. Work with engineering health metrics such as DORA and SPACE. Identify bottlenecks across the development lifecycle and use data to prioritize improvements. Translate engineering data into actionable recommendations and demonstrate the impact of proposed changes.
• Technical Leadership & Advocacy: Lead Proofs of Concept (PoCs) around new AI-native development approaches. Work directly with engineering teams to understand challenges and identify automation opportunities. Gather feedback and continuously improve the platform and its workflows. Act as a technical consultant and advocate, influencing engineering practices through technical credibility, experimentation, and data.

What We’re Looking For:

• Must-Have: 5+ years of Software Engineering experience, ideally across Backend, DevOps, Platform Engineering, or Developer Experience. 2+ years of hands-on experience with AI/Agentic systems. Strong practical knowledge of: LLM orchestration AI agents and agentic frameworks RAG architectures AI integration within software engineering workflows Strong understanding of Model Context Protocol (MCP) and AI/tool integration patterns. Experience designing developer-facing tools, automation, plugins, APIs, or internal platforms.
• Ability to work across different technologies and scripting/programming languages such as Python, TypeScript/JavaScript, Go, Bash, and PowerShell. Strong understanding of modern software development lifecycles and developer workflows. Excellent communication and stakeholder management skills. A proactive and autonomous mindset, with the ability to drive initiatives, challenge existing approaches, and influence technical decisions.
• Nice to Have: Experience building internal developer platforms, CLI tools, or developer products used by multiple engineering teams. Strong Platform Engineering / Developer Experience (DevEx) background. Experience with engineering productivity and telemetry frameworks, particularly DORA and SPACE metrics. Experience designing plugin-based or extensible architectures. Contributions to or active participation in open-source AI, LLM, or agentic ecosystems. The Profile We’re Looking For: We’re looking for more than someone who knows how to use LLM APIs.

The ideal candidate is a strong Software/Platform Engineer who has moved deeply into the AI and Agentic space and understands how to build reliable systems around LLMs. You should enjoy experimenting and building from the ground up, while still thinking about scalability, maintainability, security, developer experience, and real engineering impact. If you want to help define how engineers will work with AI—not just today, but as AI-native development becomes the standard—this could be the challenge for you.