Descripción de la oferta
Principal MLOps Architect Location: Remote from Spain (an indefinite Spanish employment contract) We 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. This 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. The position requires a strong balance between: technical leadership, hands-on implementation, AI strategy, cross-functional collaboration, and mentoring of engineering and data science teams. Requirements: Master's or Ph.D. in Computer Science, Data Science, Machine Learning, or a related field 10+ years of experience in AI/ML, data science, or distributed systems engineering. Proven experience designing and deploying production-grade AI solutions at enterprise scale. Strong background in both research and industrial AI environments. Experience leading global or distributed technical teams. Demonstrated success delivering AI transformation initiatives. Large Language Models (LLMs) Generative AI systems NLP / NLU Apache Spark Databricks Delta Lake SQL / NoSQL databases Distributed computing architectures Streaming and batch processing pipelines Azure and/or AWS Docker Kubernetes CI/CD pipelines Infrastructure-as-Code MLOps frameworks Python Scala Experience with AI governance and responsible AI practices. Experience building AI platforms serving multiple teams or business units. Experience optimizing cloud infrastructure and reducing operational costs. Responsibilities: Lead the design and implementation of AI/ML solutions across multiple business domains. Drive enterprise adoption of Large Language Models (LLMs), Generative AI, NLP/NLU, and advanced analytics solutions. Define AI architecture standards, MLOps best practices, and scalable deployment strategies. Evaluate emerging AI technologies and identify opportunities for innovation and operational impact. Translate research initiatives into production-ready AI solutions. Architect scalable distributed data-processing systems capable of handling large-scale datasets and real-time pipelines. Design and optimize cloud-native AI platforms using modern data engineering frameworks. Lead cloud migration and modernization initiatives from on-premises environments to Azure and/or AWS. Implement efficient data pipelines leveraging Spark, Delta Lake, Databricks, Kubernetes, and containerized environments. Ensure reliability, scalability, observability, and cost-efficiency of AI infrastructure. Design and implement enterprise-grade chatbot and conversational AI platforms. Lead development of Retrieval-Augmented Generation (RAG), agentic workflows, and LLM orchestration systems. Define governance, evaluation, and monitoring strategies for GenAI systems. Collaborate with research teams to operationalize LLM-based applications securely and responsibly. Lead cross-functional teams composed of data scientists, ML engineers, software engineers, and business stakeholders. Mentor engineers and researchers in AI/ML best practices, architecture, and software engineering standards. Coordinate global AI initiatives across distributed teams and multiple geographies. Communicate technical concepts effectively to executive and non-technical audiences. Support innovation programs and AI adoption strategies across the organization.
