MLOps Scientist: Privacy-Preserving Health AI

Fraunhofer Portugal Aicos; Porto; Hybrid; EUR 55,000 - 75,000; Full time

Benefits offered by this job

Partial health insurance

Flexible working hours

Hybrid work

Job summary

Fraunhofer Portugal AICOS is seeking an MLOps Scientist to join the Intelligent Systems Group, focusing on machine learning and health data analysis. You will develop privacy-preserving ML pipelines and contribute to a flexible distributed software framework supporting federated learning and data governance.

Your role includes following MLOps best practices, operating federated systems with IAM and monitoring, and implementing data governance across datasets and models; collaboration with

Qualifications

• Master's degree or equivalent in AI, AI & Data Science, Biomedical Engineering, CS & Engineering or similar.
• Minimum 18 months of relevant work experience in research departments in industry, applied research institutions, or universities.

Responsibilities

• Follow MLOps infrastructure and deployment best practices for robust ML applications, including containerization and production monitoring.
• Operationalize federated learning systems with identity and access management, client orchestration, training coordination and lifecycle management.
• Establish rules for data access, sensitivity classification, retention schedules, and audit trails.
• Manage a data governance tool for monitoring datasets and AI models under development and testing.

Skills

Python

ML frameworks

English proficiency

APIs design

DVC & MLflow

Federated Learning

Education

Master's degree in AI/CS/ biomedical engineering

Tools

Cloud platforms (AWS/GCP/Azure)

Data governance tools

Job description

Fraunhofer Portugal AICOS is seeking an MLOps Scientist to join the Intelligent Systems Group, focusing on machine learning and health data analysis. You will develop privacy-preserving ML pipelines and contribute to a flexible distributed software framework supporting federated learning and data governance.

Your role includes following MLOps best practices, operating federated systems with IAM and monitoring, and implementing data governance across datasets and models; collaboration with