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  • WIZELINE
  • Ciudad de Mexico, CMX
  • 36 days ago
WIZELINE VERIFIED EMPLOYER

Data Scientist - ML Engineering.

Data Science & ML
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Data Scientist - ML Engineering: our view in 3 lines...

  • The Role:This role is for an experienced data scientist focused on ML engineering and MLOps for AI-powered digital products and platforms.
  • The Person:The person will architect ML infrastructure, deploy models, build monitoring and retraining systems, implement model registry and audit controls, and mentor MLOps engineers.
  • Requirements:The ideal candidate has 5–8+ years in ML Engineering or MLOps and deep expertise in Spark, Azure Databricks, MLflow, Kubernetes, Docker, and Azure Pipelines.

About the role

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

  • 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.

Must-have Skills

  • 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.

Nice-to-have:

  • 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.

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.

Published October 8, 2026
Location Ciudad de Mexico, Mexico
Category Data Science & ML  

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