Arηs Group, Part of Accenture, specializes in the management of complex public sector IT projects, including systems integration, informatics and analytics, solution implementation and program management. Our team helps lead clients through digital and information systems design, bringing expertise in a variety of areas ranging from software development, data science and security management to machine learning, cloud, and mobile development.

Arηs Group was acquired by Accenture in July 2024.

ARHS, part of Accenture, is looking for a Machine Learning Engineer (m/f) with experience in Dataiku to join the team working on-site with a leading Luxembourgish bank.

You will play a key role in designing, developing, and deploying scalable AI/ML solutions within a modern data infrastructure and contribute to the transformation of critical business processes through intelligent automation and insight.

THE WORK:

• Design, build, and deploy high-quality ML models and pipelines using Dataiku and other modern frameworks.
• Migrate AI/ML solutions from local environments to the cloud, ensuring scalability and automation through CI/CD and MLOps best practices.
• Develop and expose APIs (FastAPI or Flask), optimize performance in production, and implement robust monitoring and observability.
• Collaborate with cross-functional teams to deliver end-to-end solutions aligned with business needs.
• Contribute to platform design and infrastructure improvements, supporting real-time and batch AI services.

Onsite at client site: This role requires an onsite presence with our clients and partners to support project delivery and strengthen client relationships.

Our roles require in-person time to encourage collaboration, learning, and relationship-building with clients, colleagues, and communities. As an employer, we will be as flexible as possible to support your specific work/life needs.

HERE’S WHAT YOU’LL NEED:

• Strong background in Python and experience developing ML pipelines and services.
• Hands-on experience with Dataiku DSS in production environments.
• Familiarity with cloud-native architectures (preferably Azure) and tools such as Docker, Kubernetes, and Terraform.
• Experience with building and deploying APIs (FastAPI, Flask), and integrating with enterprise data systems.
• Understanding of MLOps practices: monitoring, logging, model versioning, and CI/CD for ML.
• Good understanding of data engineering concepts and handling unstructured/streaming data.
• Excellent communication skills and ability to work closely with business and technical stakeholders.
• Fluency in English & French.