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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 / Incentivos
Private Health Insurance
Training bonus for professional development (Udemy)
23 vacation days per year
Christmas week off
Hybrid working modality
Competitive salary
Responsabilidades
Design and implement model versioning, lineage,
and validation evidence tying models to training data and clinical evidence
Draft and maintain a PCCP-style framework for permitted modifications and automated re-validation of adaptive models
Configure and manage CI/CD pipelines for training, validating, and promoting models across DTAP tiers
Develop drift and performance monitoring with triggers for retraining as part of post-market surveillance
Deploy and maintain processes for versioning and traceability aligned with design-control practices
Requisitos principales
Bachelors or Masters in CS, Math, Physics, Electrical Engineering or related fields
4–5 years of hands-on ML Ops engineering experience, preferably in medtech
Experience 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, xqbhyrx aero
📌 Machine Learning Operations Architect (Barcelona)
🏢 Jobrapido
📍 Barcelona
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