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 / 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 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, 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)
🏢 Inbrain Neuroelectronics
📍 Barcelona