At Ayvens, progress starts with you. Our ambitions to shape the future of sustainable mobility are powered by our talent. Join us, and get better with every move. At Ayvens we have an opening for an experienced AI Technical Lead. What are you going to do? As our new AI Technical Lead, you will provide the technical leadership and hands-on MLOps expertise required to define, operationalize, validate and scale AI solutions across Ayvens. You will support strategic AI decisions while remaining directly involved in the MLOps implementation layer, covering technology choices, architecture, platform capabilities, infrastructure as code, integration, deployment, technical staffing and production readiness. You will translate business and governance needs into working MLOps patterns and ensure that AI solutions are secure, interoperable, scalable, maintainable and aligned with the target technology landscape. You will report to the Director Data Science & AI. Your responsibilities will include:

Technical leadership and decision support: Act as the senior technical reference for AI and data science initiatives. Provide clear recommendations on architecture, platforms, build-versus-buy decisions, reuse opportunities, technical feasibility, engineering effort and delivery risks. AI platform strategy and architecture: Lead the evolution of the AI platform landscape, including MLOps, LLMOps, Azure AI services, AI Foundry, Databricks, the SG Agentic Platform and related engineering capabilities. Define and implement target patterns that enable secure and scalable industrialization. MLOps capacity and staffing: Assess the MLOps and platform profiles, skills, capacity and delivery model required to operationalize AI initiatives. Support workforce and sourcing decisions,



challenge staffing assumptions and help structure effective collaboration between MLOps, Data Science, AI Engineering, Data Engineering, Architecture and infrastructure teams. MLOps solution design, implementation and technical assurance: Design, configure, review and challenge the operationalization layer across model packaging, APIs, serving endpoints, integrations, cloud infrastructure, security, deployment and operations. Contribute directly to reference MLOps implementations, platform configurations, deployment pipelines and reusable operational components. Ensure solutions meet quality, resilience, performance, interoperability and maintainability expectations. Industrialization and operations: Establish and implement engineering standards for CI/CD, testing, code quality, model and prompt evaluation, observability, monitoring, versioning, deployment, incident management, disaster recovery and lifecycle maintenance. Build and maintain reusable MLOps/LLMOps patterns for the operationalization of AI solutions. AI governance and compliance: Provide technical input to AI governance decisions and ensure architecture, security, privacy, MRM, AI Act and internal control requirements are translated into implementable engineering controls and evidence. Technical roadmap and investment planning: Contribute to technology roadmaps, platform investments and engineering budgets. Provide transparent estimates and options, including cost, resource implications,



dependencies and trade-offs. Stakeholder communication: Explain complex technical topics, options and risks in concise business language. Facilitate decisions with senior leaders, Product, Architecture, Security, Risk, Legal, countries and delivery teams, and document clear recommendations and ownership. MLOps coaching and standards: Guide MLOps specialists and technical leads, promote reusable platform assets and reference architectures, and raise MLOps maturity through practical standards, platform reviews and knowledge sharing. Work closely with the AI Engineering team without owning AI application engineering delivery. Who are you?

The adecuado candidate is a senior MLOps and AI platform leader who combines deep hands‑on operational expertise with strong judgment, communication and organizational influence. You can move comfortably from detailed MLOps implementation discussions to executive decision forums, and you can challenge platform, operationalization and resourcing assumptions with clarity and credibility. Typically 10+ years of relevant experience across technology, data platforms or AI, including substantial hands‑on experience leading the MLOps architecture, industrialization and operation of production AI platforms in complex international environments. Proven technical leadership and hands‑on delivery across MLOps/LLMOps, AI/ML platforms, cloud architecture, infrastructure automation, model operationalization and production operations. Strong experience defining target architectures, technical standards and engineering roadmaps, and making pragmatic technology decisions under constraints. Demonstrated ability to assess engineering effort, staffing needs, required profiles and del

📌 Experienced AI Tech Lead (Madrid)
🏢 ALDES ALD Automotive
📍 Madrid