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Tuio is seeking a Forward Deployed AI Engineer to join its AI & Data team and build production-grade artificial intelligence solutions across the organization. The role combines discovery work with hands-on AI engineering, requiring deep collaboration with business teams to understand real-world problems and design agent-based systems that solve them.
Day-to-day responsibilities include working directly with business stakeholders to understand processes, systems, data, and risks; designing architectures for agent-based systems using appropriate models, patterns, and components; building language model applications leveraging context engineering, tool calling, structured outputs, RAG, and multi-step workflows; experimenting with models and architectures to optimize quality, latency, cost, and reliability; connecting agents to internal and external tools via APIs, MCP servers, databases, and SaaS platforms; rapidly prototyping and evolving solutions with real-world data; designing evaluations, test cases, and regression tests; implementing reliability mechanisms including permissions, validations, retries, fallbacks, and human escalation; deploying solutions using CI/CD and sound engineering practices; and turning lessons learned into reusable components and patterns.
You will become embedded in one of Tuio's priority domains, taking responsibility for one or two use cases and bringing at least one from prototype to pilot with real users. The role emphasizes end-to-end technical ownership, from discovery through deployment and monitoring. You will balance rapid experimentation with rigorous measurement and strengthened controls as risk increases. The goal is demonstrating that agents can improve real-world operations in measurable, reliable, and responsible ways—not simply launching demos.
Required qualifications: university degree in Engineering, Mathematics, Physics, or related technical/quantitative discipline; at least five years in AI Engineering, Applied AI, Software Engineering, Product Engineering, Data, or similar technical role; strong Python programming foundation; experience building language model applications with understanding of context engineering, tool calling, structured outputs, RAG, evaluations, and human-in-the-loop systems; experience with APIs, databases, authentication, testing, Git, and cloud-deployed services; ability to turn ambiguous problems into architecture and initial implementation; ability to use data, metrics, traces, and evaluations to analyze and improve AI system behavior; strong engineering judgment balancing quality, latency, cost, security, maintainability, and development speed; interest in working closely with users; fluency in Spanish and English with both technical and business stakeholders.
Desirable: experience deploying agents or language-model-based products used by real users; experience with tool calling, RAG, MCP servers, vector databases, workflows, and related technologies.
