At Ayvens we have an opening for an experienced AI Technical Lead. 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.
Technical leadership and decision support: Act as the senior technical reference for AI and data science initiatives. 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. 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. 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. 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. 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. Facilitate decisions with senior leaders, Product, Architecture, Security, Risk, Legal, countries and delivery teams, and document clear recommendations and ownership. 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. 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. Strong hands-on MLOps experience with Python, SQL, REST APIs, Git-based workflows, CI/CD, containers, model serving, monitoring, automated testing and cloud platform practices. The candidate must be able to review deployment pipelines and platform configurations, create MLOps prototypes and support production operationalization directly. Hands-on experience with Azure and Databricks ecosystems, including Azure AI Foundry, MLflow, Unity Catalog, GitHub and agentic AI platforms. Strong knowledge of security, data privacy, model risk, responsible AI, resilience, disaster recovery and regulatory requirements relevant to enterprise AI.
Willing and able to work directly within the MLOps team, inspect repositories, Infrastructure-as-Code configurations and deployment pipelines, troubleshoot platform and operationalization issues, build reference MLOps implementations and turn architecture decisions into executable platform standards and reusable assets. Demonstrated experience moving models and GenAI solutions from experimentation into production, including packaging, deployment, orchestration, evaluation, observability, rollback, versioning, performance management and ongoing support. Ability to integrate AI capabilities with enterprise applications, data platforms, authentication, logging and monitoring services. Infrastructure as code and cl

📌 Tech Lead Azure (Comunidad de Madrid)
🏢 Ayvens-1
📍 Comunidad de Madrid