AI Engineer
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 should own it. You combine curiosity about new capabilities with the engineering discipline needed to make them operational. You are comfortable working in collaborative, international teams and alongside client stakeholders.
🛠️ What You Bring Qualifications & Experience Degree in Computer Science, Engineering or a related field, or equivalent professional experience. Strong hands-on experience building production GenAI / LLM-based systems , beyond notebook-based or demonstration work. Experience deploying and operating AI capabilities in production , including monitoring, reliability, latency, cost and continuous improvement. Ability to work effectively in multidisciplinary teams alongside software, data, architecture and governance specialists.
Availability for monthly travel to Lisbon or Madrid . Professional fluency in Portuguese, Spanish and English , written and spoken. Technical & Professional Skills Strong Python and software-engineering fundamentals , including APIs, testing, version control, code review and maintainable production code. Practical experience designing and improving RAG and retrieval-based systems , including context preparation, retrieval quality and grounding.
Hands-on experience engineering AI agents or multi-step AI workflows , including tool use, context management, routing, state, failure handling and human control. Experience creating evaluations for LLM or agent systems , using measurable criteria and representative test cases to assess behaviour and detect regressions. g. CRM, ERP, MCP, webhooks ) Ability to tie agent behaviour to the business KPI it serves. Working knowledge of solution-level guardrails, validation, human oversight and responsible AI engineering practices.
Familiarity with at least one major cloud or AI ecosystem , plus containerisation, CI/CD, observability and LLMOps practices, and the ability to optimise for quality, latency, context usage and inference cost. Depth in one or more ecosystems such as LangGraph, LangChain, LlamaIndex or Semantic Kernel ; OpenAI, Anthropic, Google, Microsoft or open-source models ; and pgvector, Pinecone, Weaviate or Milvus . These are examples, not a checklist.
Nice to Have Deeper agentic AI experience , including multi-agent orchestration, planning and execution loops, complex state management and recovery strategies. Experience with multimodal systems, fine-tuning, open-source model deployment or specialised inference. Classical machine-learning or deep-learning experience. Low-code agent platforms such as n8n, Make or Dust for fast automations alongside code. Experience in regulated or security-sensitive environments.
Consulting or client-facing experience, and exposure to international projects or multicultural teams. Experience mentoring engineers or creating reusable AI engineering standards, components or evaluation approaches. ⭐ What Makes You Successful You use evaluations and production evidence to guide engineering decisions. You balance quality, reliability, latency, cost, safety and maintainability. You diagnose unexpected AI behaviour systematically rather than treating model output as a black box.
You understand your decision authority and involve the right specialist when a decision belongs to architecture, software, data, governance or delivery. You communicate technical trade-offs clearly and pragmatically. You turn successful engineering patterns into reusable knowledge that improves future delivery.
20/day Flexible benefits plan Private medical insurance 22 days of annual leave, increasing every 3 years (up to 25 days) Continuous learning via KLX – Keyrus Learning Experience A collaborative, international, and human-centred work environment 💰 How Our Salary Ranges Work At Keyrus, salary ranges reflect different levels of mastery and impact within the same role — not different job titles. Bottom of the range You meet the core requirements and will need ramp-up time and support.
Middle of the range You are fully autonomous from Day 1 and deliver consistently. Top of the range You are a reference for the role, mentor others, and raise the bar for the team. Final offers are based on experience, autonomy, scope, and market context , and are discussed transparently during the process. 🔒 Responsible AI & Recruitment At Keyrus, all stages of our recruitment process are conducted and evaluated by human recruiters and interviewers.
To support accuracy and efficiency, AI may occasionally be used internally by our team exclusively for note-taking purposes during interviews . AI is never used to make decisions. To ensure fairness, authenticity, and the protection of confidential and proprietary information , the use of AI tools by candidates during the recruitment process is strictly prohibited . Our commitment to responsible AI practices ensures that hiring decisions are based solely on each candidate’s own skills, experience, judgment, and expertise.
⚠️ Any use of AI assistance during the interview process may result in immediate disqualification from the recruitment process. ♿ Equal Opportunity Statement We are committed to building an inclusive workplace and encourage applications from all backgrounds, regardless of race, ethnicity, gender identity, sexual orientation, age, disability, or any other protected characteristic.