Senior AI Platform Engineer

emagine - Portugal; Portugal; Presencial; EUR 70 000 - 95 000; Tempo integral

Resumo da oferta

emagine - Portugal is seeking a Senior AI Platform Engineer to join the DevEx team and help build AI-native workflows and agentic platforms for a large R&D organization. You will design the frameworks, agents, integrations, and abstractions that enable engineering teams to work more effectively with AI.

You will lead the design of multi-agent systems, RAG solutions, and reusable components while ensuring security, governance, and scalability across the developer experience.

Qualificações

• 5+ years of Software Engineering experience.
• 2+ years hands-on experience with AI/Agentic systems.
• Strong knowledge of LLM orchestration and MCP.
• Experience designing developer-facing tools, APIs, or internal platforms.

Responsabilidades

• Architect and implement AI-native and agentic workflows.
• Design multi-agent systems and RAG solutions.
• Develop evaluation frameworks and feedback loops for AI outputs.
• Lead PoCs and propagate automation opportunities with engineering teams.
• Create reusable Skills, Agents and Commands for the software lifecycle.
• Ensure security, governance, and maintainability of the platform.
• Define telemetry and leverage DORA/SPACE metrics to improve performance.

Conhecimentos

Software engineering

AI/Agentic systems

LLM orchestration

AI agents

MCP

Developer-facing tools

DevOps/Platform Engineering

Communication

Proactive mindset

Descrição da oferta de emprego

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:

• 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.
• 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.
• 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.
• 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
• 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.
• 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.