Descripción de la oferta
Principal MLOps ArchitectLocation: 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.
