MedTech MLOps Architect: Change-Control & Deployment

Tamarind Intelligence; Barcelona; Híbrido; EUR 70.000 - 110.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Private Health Insurance

Hybrid working modality

Training bonus

Udemy access

23 vacation days

Descripción de la vacante

INBRAIN in Barcelona is seeking a Machine Learning Operations Architect to own model lifecycle governance and deployment infrastructure. You will bridge MLOps engineering with medical-device design-control practice: it defines how models are versioned, validated, and promoted, and builds the change-control framework (aligned with a PCCP-style approach) that governs adaptive models post-deployment.

You will lead post-market surveillance with drift monitoring, CI/CD for ML, and collaboration with

Formación

• Bachelor's or Master's degree in CS/Math/Physics/EE or related field.
• 4-5 years hands-on ML Ops in medtech or safety-critical contexts.
• Experience shipping ML in regulated environments (IEC 62304, ISO 13485, DO-178C, ISO 26262).
• Strong IaC skills and productionizing research code (Python).
• Fluency in English; company language.
• Bridge engineering with regulatory/clinical teams.

Responsabilidades

• Design model versioning, lineage, and validation evidence system.
• Draft PCCP-style change control framework with automated re-validation.
• Configure CI/CD pipeline for training, validation, and promotion across DTAP.
• Build drift and performance monitoring with retraining triggers.
• Maintain processes and services versioning with design-control alignment.

Conocimientos

ML Ops

Model versioning

CI/CD for ML

Kubernetes

Argo

Python

Regulatory frameworks

English fluency

Educación

Bachelor's or Master's degree in Computer Science, Mathematics, Physics, Electrical Engineering or related field

Herramientas

MLFlow or equivalent

Kubernetes

Argo

Descripción del empleo

INBRAIN in Barcelona is seeking a Machine Learning Operations Architect to own model lifecycle governance and deployment infrastructure. You will bridge MLOps engineering with medical-device design-control practice: it defines how models are versioned, validated, and promoted, and builds the change-control framework (aligned with a PCCP-style approach) that governs adaptive models post-deployment.

You will lead post-market surveillance with drift monitoring, CI/CD for ML, and collaboration with