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 / 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, xqziphu aerospace) and design-control frameworks (IEC 62304, ISO 13485, DO-178C, ISO 26262)
Infrastructure-as-code proficiency
Python productionization and supporting data scientists in production
Fluency in English
Experience with continuous/adaptive learning systems under change-control frameworks
Ability to translate design-control requirements into technical implementations
Pragmatic with scope and sequencing; strong documentation skills
Strong written and verbal communication
Cross-functional collaboration
Pragmatism and prioritization
MLFlow or equivalent model registries
CI/CD for ML
Kubernetes

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