AI Engineer Data Analytics

Why Keyrus, Why Now

Keyrus is an international group of 2,800 consultants and experts across 28 countries, built on a single conviction: AI does not transform businesses. Architected intelligence does.

For more than 30 years, we have been building the data foundations that make intelligent systems work — designing the Operating System of the intelligent enterprise, where intelligence is embedded into the core of business processes to create sustainable value: we operationalise intelligence.

AI does not replace humans. It repositions us to a place no system can follow: understanding, deciding, designing, and creating.

At Keyrus, you will not just develop skills — you will develop judgment. Your expertise sharpens with every system you architect, every client challenge you solve, and every deployment that compounds on the last.

Over time, you grow into one of the rarest professionals of the intelligence era: someone who bridges data, AI, and human decision‑making at scale, across industries and geographies. This is not a role you fill. It is a discipline you master and a story you help write to become a Keyrus Architect of Intelligence.

Technology amplifies. Keyrus culture differentiates. Industrial discipline connects the two.

Role Details

Job location: Portugal (visits to Lisbon or Madrid Office when needed)
Contract type: Employee
Target start date: January 2027
Working hours: Full‑time
Compensation: €42k–€58k per year

What You'll Architect

As an AI Engineer , you will help organisations transform complexity into measurable outcomes by combining technology, data, intelligence, and human decision‑making.

This role is both technical and consultative — perfect for someone who can combine hands‑on AI engineering, solution design, and close collaboration with client teams. We’re looking for professionals who think like architects and act like builders.

Your focus is the intelligence layer of the solution: how models are integrated, how information is retrieved, how agents behave, how quality is evaluated, and how the capability performs once deployed. You will work closely with Software Engineers, Data Engineers, AI Foundation Architects, Forward Deployed Engineers and AI Governance specialists , while retaining clear ownership of the AI behaviour and quality of the solution.

• Design and build production AI capabilities , including RAG systems, intelligent assistants and agent‑enabled workflows aligned with the approved solution architecture.

• Engineer agent behaviour across instructions, context, tools, state, routing, memory, recovery, stopping conditions and appropriate human checkpoints.

• Integrate and configure models through APIs or supported platforms, making the right trade‑offs between capability, quality, latency, cost and operational requirements.

• Design evaluations and quality gates using representative datasets, automated and regression testing, task‑success measures, groundedness and other relevant indicators.

• Deploy, monitor and improve AI capabilities in production , using evaluation results and observed system behaviour to drive continuous improvement.

• Implement reliability and control mechanisms such as validation, guardrails, failure handling and human oversight, within the architecture and governance standards defined for the engagement.

• Integrate AI capabilities with the surrounding solution and client systems , working with Software Engineers on application interfaces and with Data Engineers on retrieval, data quality and AI‑ready data.

• Enable client teams to run and extend the agents after go‑live.

• Document and reuse what works , including AI behaviour, dependencies, evaluation criteria, limitations and engineering patterns that strengthen future Keyrus engagements.

Who You Are

• You are a hands‑on engineer who understands that a model is only one part of an intelligent system.

• You care about what happens after the model responds: whether the context was right, whether the agent chose the appropriate action, and whether the result holds up when it runs repeatedly in production.

• You make pragmatic technical trade‑offs rather than searching for the perfect technology choice.

• You use evidence and evaluations to improve systems, and you communicate limitations clearly.

• You know when a problem belongs to AI Engineering and when another specialist s