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What makes this a great opportunity?
This is a hands:on director:level technical leadership role with a clear mission: identify, design, and validate the next enterprise AI capabilities with the greatest potential to create business value.
The Director of Core AI Engineering will set the technical direction and operating model for emerging and complex enterprise AI capabilities, combining accountable technical leadership with direct involvement in architecture, critical engineering decisions, prototypes, enterprise integrations, code and design reviews, and technical validation.
You will lead a small, highly capable team and partner closely with AI Product Delivery, data, security, infrastructure, and application:engineering leaders to prove what is technically possible, resolve key architectural and integration questions, and create the technical blueprint and evidence needed for capabilities to be industrialized by delivery teams.

Role Responsibilities
Set the Core AI engineering direction

:Own the technical exploration roadmap, target architecture, and engineering standards for emerging enterprise AI capabilities.
:Define architectural boundaries, design assumptions, and technical decision criteria in partnership with Product Delivery, data, security, infrastructure, and engineering teams.




:Make pragmatic decisions on architecture, model access and routing, orchestration, retrieval, integrations, and build:versus:buy choices.
:Create reusable reference patterns for AI orchestration, enterprise integrations, evaluation, observability, security, context management, and LLMOps.
Own the advanced AI systems stack

:Architect and validate agent runtimes, including single: and multi:agent orchestration, workflow routing, human:in:the:loop controls, retries, fallbacks, and recovery mechanisms.
:Design and test RAG and enterprise retrieval approaches, including ingestion, embeddings, vector indexing, hybrid retrieval, access controls, reranking, and knowledge:graph approaches.
:Prototype context:engineering, caching, memory, tool:using AI systems, and closed:loop improvement mechanisms.
:Establish viable model:serving and inference designs, including gateways, routing, latency, scalability, cost, performance, and fallback requirements.
:Select and test fit:for:purpose technologies based on measurable technical evidence.
Lead technical incubation and validated handoff

:Lead prioritized use cases through discovery, prototype, enterprise integration, evaluation, security review, and technical handoff.
:Remain hands:on in architecture, design and code reviews, critical prototypes, technical problem solving, and readiness decisions.
:Define reference implementations, interfaces, quality evidence, non:functional requirements, limitations, and acceptance criteria for industrialization.
:Establish clear handoff and knowledge:transfer points with delivery, platform, security, and operations teams.
Validate quality, safety, and technical readiness

:Establish measurable evaluation approaches covering quality, groundedness, retrieval performance, tool reliability, safety, latency, cost, and regression detection. xghoner
:Instrument prototypes to capture system behaviour, usage, failures, traces, model and prompt versions, latency, cost, and user feedback.
:Partner with security and data:governance teams to implement appropriate controls for sen

📌 Director Core AI Engineering: Based in Madrid
🏢 Empresa Confidencial
📍 Madrid