Overview

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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 / Ventajas
- 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
- Bachelor's or Master's 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 envir

📌 Machine Learning Operations Architect (Barcelona)
🏢 Jobrapido
📍 Barcelona