Cloud Engineer ( Data Science) | Proximus Ada

Editx; Schaarbeek; Sur place; EUR 60 000 - 85 000; Plein temps

Résumé du poste

Editx is seeking a Cloud Engineer to enhance its team. You will design, deploy, and scale cloud-based AI and data solutions, utilizing your strong background in DevOps, MLOps, and Azure cloud architecture.

Your role includes automating processes for the ML lifecycle, implementing best practices for CI/CD pipelines, and collaborating with diverse teams to deliver robust platforms tailored to our clients' needs. Join us to ensure security and operational excellence in our cloud solutions.

Qualifications

• Strong background in DevOps and MLOps.
• Experience with cloud architecture, specifically Azure.
• Ability to implement Infrastructure as Code practices.

Responsabilités

• Architect and design cloud solutions and platforms using Azure.
• Automate the ML lifecycle to ensure efficiency and scalability.
• Manage resources using Infrastructure as Code practices.
• Implement CI/CD pipelines for reliable deployment of ML models.
• Collaborate with cross-functional teams to define requirements.
• Challenge existing security measures and enhance defenses.

Connaissances

DevOps

MLOps

Cloud Architecture (Azure)

Infrastructure as Code (IaC)

CI/CD Pipelines

Description du poste

We are seeking a Cloud Engineer with a strong background in DevOps, MLOps, and cloud architecture (Azure) to join our team. You will play a pivotal role in designing, deploying, and scaling cloud-based AI and data solutions, leveraging Infrastructure as Code (IaC) and modern DevOps practices. Your expertise will help us deliver robust platforms and services for a wide range of client projects, ensuring security, scalability, and operational excellence.

Responsibilities

• Architect and design cloud solutions and platforms (Azure), translating business needs into scalable, secure, and cost-effective architectures.
• Automating everything related to the ML lifecycle to ensure efficiency and scalability.
• Using Infrastructure as Code (IaC) practices, such as Bicep, to manage and provision resources through code.
• Implementing best practices for continuous integration and delivery (CI/CD) pipelines, ensuring that ML models are reliably deployed to production environments.
• Collaborate with data scientists, AI engineers, and business stakeholders to define requirements, select technologies, and deliver end-to-end solutions.
• Challenge our existing security and reinforce our defenses.