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.