JobCubby JobCubby
Sign up
WIZELINE

Data Scientist - ML Engineering

WIZELINE Ciudad de México, Mexico

IT Services and IT Consulting · 1,001-5,000 employees

6 h ago
data-scientist Senior (5-10 yrs) Full-time Mexico
Create a free account to apply — email only, no card. You can also save this posting or score it against your profile with AI.

Applying here? Try the free cover letter tool — paste this posting and your résumé, no account needed.

About the role

Architect and lead end-to-end ML infrastructure, including deployment, monitoring, and governance of high-impact models. Drive the adoption of MLOps best practices and mentor cross-functional teams on platform integration.

What they look for

ML Engineering MLOps Spark Azure Databricks MLflow Kubernetes Docker Azure Pipelines CI/CD NLP Forecasting Optimization Feature engineering Feature stores Containerization Observability

Requirements

Requires 5-8+ years of experience in ML Engineering or MLOps with deep expertise in Spark, Azure Databricks, and containerization technologies. A proven track record of deploying enterprise-scale ML systems with robust monitoring and audit capabilities is essential.

Benefits

Professional development Flexible culture Collaborative environment Global opportunities Total rewards

Full description

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.

Similar roles