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.
