Senior MLOps Engineer

What project we have for you

We are searching for an ML Engineer that can support our Data Science team on an interim basis developing, implementing and maintaining AI/ML solutions. The work will have a clear focus on defining technical requirements and implementing those solutions at scale, as well as implementing data pipelines for feature engineering. A close collaboration with Data Scientists and Data Engineers ensures that the Business Requirements are understood, and the solution is connected to the data flow.

Team Information:

We are a team of 4 Data Scientists working on solutions for the business. Among others, we developed algorithms for predicting the probability of default of our customers; churn; fraud; cross-and-upselling potential. We are working agile (2week sprints) and enjoy discussions over the topics at hand. Each Data Scientist is currently responsible for different projects and products. The ML Engineer will be required to support multiple projects/products and work with the entire team.

What you will do

• Build and maintain the underlying infrastructure for our Sales Transformation AI models.
• Primary goal is to ensure that the models operate in a secure, scalable, and automated environment.
• You will be responsible for the “industrialization” of the platform—ensuring that security standards are met, pipelines are fail-safe, and the infrastructure supports high-availability requirements for real-time sales triggers.

What you need for this

Must Have:

Infrastructure as Code (IaC): Experience in provisioning and managing Azure resources (Azure ML, Key Vault, Storage Accounts) using Terraform or Bicep.

• Automation & CI/CD: Expert knowledge in building and maintaining Azure DevOps pipelines specifically for ML (automated testing, deployment, and model versioning).
• Security & Compliance: Proficiency in implementing security standards for sensitive data, including RBAC, encryption at rest/transit, and securing workspaces within Azure VNETs.
• Monitoring & Observability: Experience setting up monitoring for model drift, system performance, and cost tracking (e.g., Azure Monitor, Application Insights).
• Containerization: Strong knowledge of Docker and Azure Kubernetes Service (AKS) or Azure Container Instances (ACI) for model serving.
• Collaborative Mindset: Ability to define “Production Readiness” criteria and guide ML Engineers on best practices for code modularity and scalability.

Nice-to-Have:

• Certification in Azure Solutions Architecture or Azure AI Engineer.
• Experience with MLflow for experiment tracking and model registry.

Skills

• Azure
• ML
• Python
• SQL