We are:
Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI, accelerating market entry and driving business transformation. As a global community of innovators, we foster a culture of growth, collaboration, and impact.
With the right people and the right ideas, there’s no limit to what we can achieve
Are you a fit?
Sounds awesome, right? Now, let’s make sure you’re a good fit for the role:
Key Responsibilities:
Design and develop MLOps pipelines for model training, deployment, and retraining.
Containerize models using Docker and deploy via Azure Databricks or AKS.
Implement CI/CD workflows with MLflow and GitHub Actions.
Monitor model performance and data drift using Azure-native tools.
Collaborate with Data Scientists and Engineers to integrate models into business systems.
Document, standardize, and optimize ML deployment processes.
Must-have Skills:
Bachelor's in Computer Science, Data Engineering, or a related field.
3–5 years of experience in MLOps, ML Engineering, or DevOps for ML.
Proficient in Spark and MLflow; strong experience in Databricks and Azure ML.
Solid Python and SQL skills; knowledge of containers (Docker/Kubernetes).
Familiar with CI/CD concepts and tools like Azure DevOps or GitHub Actions.
Nice-to-have:
Familiarity with Kubernetes (AKS), Terraform, and model observability practices.
Experience deploying Power BI dashboards that consume predictions.
What we offer:
A High-Impact Environment
Commitment to Professional Development
Flexible and Collaborative Culture
Global Opportunities
Vibrant Community
Total Rewards
Specific benefits are determined by the employment type and location.
Find out more about our culture here.