PPrincipal MLOps Architect /ppLocation: Remote from Spain (an indefinite Spanish employment contract) /ppbr/ppWe are seeking a highly experienced and hands-on Principal MLOps Architect Applied AI Lead to drive the design, development, and operationalization of enterprise-scale AI systems across research and production environments. /ppThis role combines deep technical expertise in Machine Learning, Generative AI, distributed data systems, and cloud-native architectures with strategic leadership capabilities. The ideal candidate will lead complex AI initiatives end-to-end — from experimentation and research to scalable deployment in global enterprise environments. /ppbr/ppThe position requires a strong balance between: /pullitechnical leadership, /lilihands-on implementation, /liliAI strategy, /lilicross-functional collaboration, /liliand mentoring of engineering and data science teams. /li /ulpbr/ppbRequirements: /b /pulliMaster's or Ph.D. in Computer Science, Data Science, Machine Learning, or a related field /lili10+ years of experience in AI/ML, data science, or distributed systems engineering. /liliProven experience designing and deploying production-grade AI solutions at enterprise scale. /liliStrong background in both research and industrial AI environments. /liliExperience leading global or distributed technical teams. /liliDemonstrated success delivering AI transformation initiatives. /liliLarge Language Models (LLMs) /liliGenerative AI systems /liliNLP / NLU /liliApache Spark /liliDatabricks /liliDelta Lake /liliSQL / NoSQL databases /liliDistributed computing architectures /liliStreaming and batch processing pipelines /liliAzure and/or AWS /liliDocker /liliKubernetes /liliCI/CD pipelines /liliInfrastructure-as-Code /liliMLOps frameworks /liliPython /liliScala /liliExperience with AI governance and responsible AI practices. /liliExperience building AI platforms serving multiple teams or business units. /liliExperience optimizing cloud infrastructure and reducing operational costs. /li /ulpbr/ppbResponsibilities: /b /pulliLead the design and implementation of AI/ML solutions across multiple business domains. /liliDrive enterprise adoption of Large Language Models (LLMs), Generative AI, NLP/NLU, and advanced analytics solutions. /liliDefine AI architecture standards, MLOps best practices, and scalable deployment strategies. /liliEvaluate emerging AI technologies and identify opportunities for innovation and operational impact. /liliTranslate research initiatives into production-ready AI solutions. /liliArchitect scalable distributed data-processing systems capable of handling large-scale datasets and real-time pipelines. /liliDesign and optimize cloud-native AI platforms using modern data engineering frameworks. /liliLead cloud migration and modernization initiatives from on-premises environments to Azure and/or AWS. /liliImplement efficient data pipelines leveraging Spark, Delta Lake, Databricks, Kubernetes, and containerized environments. /liliEnsure reliability, scalability, observability, and cost-efficiency of AI infrastructure. /liliDesign and implement enterprise-grade chatbot and conversational AI platforms. /liliLead development of Retrieval-Augmented Generation (RAG), agentic workflows, and LLM orchestration systems. /liliDefine governance, evaluation, and monitoring strategies for GenAI systems. /liliCollaborate with research teams to operationalize LLM-based applications securely and responsibly. /liliLead cross-functional teams composed of data scientists, ML engineers, software engineers, and business stakeholders. /liliMentor engineers and researchers in AI/ML best practices, architecture, and software engineering standards. /liliCoordinate global AI initiatives across distributed teams and multiple geographies. /liliCommunicate technical concepts effectively to executive and non-technical audiences. /liliSupport innovation programs and AI adoption strategies across the organization. /li /ul