AI Engineer

As an AI Engineer, you will help organisations transform complexity into measurable outcomes by combining technology, data, intelligence, and human decision-making. You will work closely with Software Engineers, Data Engineers, and AI Foundation Architects, 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

• 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
• 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, grounding and relevant data dependencies
• 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
• Experience integrating commercial or open-source model providers, and integrating AI with enterprise applications, APIs, data sources and workflow systems
• 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
• 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

• Deeper agentic AI experience, including multi-agent orchestration, planning and execution loops, complex state management, recovery strategies and sophisticated human-in-the-loop patterns
• 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

• Compensation: €42k–€58k per year
• Meal allowance: €10.20/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

Resumo organizado por IA a partir do anúncio original. Confirma os detalhes no site da empresa.