Data Scientist - ML Engineering

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.

Sounds awesome, right? Now, let’s make sure you’re a good fit for the role:

• →Architect end-to-end ML infrastructure across pipelines, serving, monitoring, and governance.
• →Lead deployment of high-impact models (forecasting engines, optimization solvers, NLP models).
• →Design advanced CI/CD workflows using Azure Pipelines, MLflow, and Databricks.
• →Implement model registry, versioning, lineage, and audit compliance.
• →Build monitoring systems for model drift and retraining automation.
• →Mentor MLOps engineers and guide cross-functional platform integration.
• →Drive adoption of MLOps best practices, from containerization to observability.

• 5–8+ years in ML Engineering, MLOps, or high-scale ML systems.
• Deep expertise in Spark, Azure Databricks, MLflow, Kubernetes, and Docker.
• Proven track record deploying ML at enterprise scale with audit and monitoring layers.
• Familiarity with hybrid/multi-cloud infrastructure.

• AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work, including drafting, analysis, research, or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows.
• Leadership experience in ML platform or DevOps teams.
• Experience with feature stores and feature engineering. AutoML is a plus, H2O is a plus.