Experienced AI Technical Lead (MLOps)

About the role

Ayvens, a company focused on sustainable mobility, is looking for an experienced AI Technical Lead in Madrid. The role provides technical leadership and hands-on MLOps expertise to define, operationalize, validate and scale AI solutions, reporting to the Director Data Science & AI.

Key responsibilities

• Act as senior technical reference for AI and data science initiatives, advising on architecture, platforms, build-versus-buy decisions and delivery risks.
• Lead the AI platform landscape (MLOps, LLMOps, Azure AI services, AI Foundry, Databricks, SG Agentic Platform) and define secure, scalable patterns.
• Assess MLOps capacity and staffing, and support collaboration between MLOps, Data Science, AI Engineering, Data Engineering, Architecture and infrastructure teams.
• Design, configure and review the operationalization layer: model packaging, APIs, serving endpoints, integrations, cloud infrastructure, security, deployment and operations.
• Establish engineering standards for CI/CD, testing, code quality, observability, monitoring, versioning, incident management and disaster recovery.
• Provide technical input to AI governance, security, privacy, MRM, AI Act and internal control requirements.
• Contribute to technology roadmaps, platform investments and engineering budgets, and explain technical options to senior leaders and other stakeholders.
• Coach MLOps specialists and technical leads, promote reusable platform assets and raise MLOps maturity.

Required profile

• Typically 10+ years of experience in technology, data platforms or AI, including leading MLOps architecture and production AI platforms in complex international environments.
• Proven technical leadership and hands-on delivery across MLOps/LLMOps, cloud architecture, infrastructure automation and model operationalization.
• Bachelor's or Master's degree in a technical discipline; fluent English is required.
• Experience in mobility, leasing, financial services or another regulated industry is an advantage.

Required skills

• Hands-on MLOps with Python, SQL, REST APIs, Git, CI/CD, containers, model serving, monitoring and automated testing.
• Azure and Databricks ecosystems, including Azure AI Foundry, MLflow, Unity Catalog, GitHub and agentic AI platforms.
• Infrastructure as code with Terraform, integrated into secure CI/CD pipelines.
• Security, data privacy, model risk, responsible AI and regulatory requirements.