Overview As a Machine Learning Operations Architect, you will own the model lifecycle governance and deployment infrastructure for safety-critical AI in neurotechnology. You will define how models are versioned, validated, and promoted, and create a PCCP-style change-control framework enabling safe, adaptive updates post-deployment. You’ll bridge MLOps with medical-device design-control, collaborating with clinical, regulatory, product, and software stakeholders. This role places you at the intersection of engineering and regulation to bring advanced healthcare solutions to market. You will shape how real-time models impact patients and the broader neurotech mission.

Compensaciones / BeneficiosPrivate Health InsuranceTraining bonus for professional development (Udemy)23 vacation days per yearChristmas week offHybrid working modalityCompetitive salary ResponsabilidadesDesign and implement model versioning, lineage, and validation evidence tying models to training data and clinical evidenceDraft and maintain a PCCP-style framework for permitted modifications and automated re-validation of adaptive modelsConfigure and manage CI/CD pipelines for training, validating,



and promoting models across DTAP tiersDevelop drift and performance monitoring with triggers for retraining as part of post-market surveillanceDeploy and maintain processes for versioning and traceability aligned with design-control practices Requisitos principalesBachelor's or Master's in CS, Math, Physics, Electrical Engineering or related fields4–5 years of hands-on ML Ops engineering experience, preferably in medtechExperience with ML model registries (MLFlow or equivalent) and ML CI/CD, containerized deployment (Kubernetes)Workflow orchestration (Argo or equivalent)Proven experience in regulated or safety-critical environments (medtech, automotive, aerospace) and design-control frameworks (IEC 62304, ISO 13485, DO-178C, ISO 26262)Infrastructure-as-code proficiencyPython productionization and supporting data scientists in productionFluency in EnglishExperience with continuous/adaptive learning systems under change-control frameworksAbility to translate design-control requirements into technical implementationsPragmatic with scope and sequencing; strong documentation skillsStrong written and verbal communicationCross-functional collaborationPragmatism and prioritizationMLFlow or equivalent model registriesCI/CD for MLKubernetes

📌 Machine Learning Operations Architect (with medical device experience) (Barcelona)
🏢 Jobrapido
📍 Barcelona