Data Scientist - ML Engineering

Wizeline; Ciudad de México; On-site; MXN 900,000 - 1,500,000; Full time

Benefits offered by this job

Professional development

Flexible culture

Total rewards

Job summary

Wizeline in CDMX is seeking an experienced ML Engineer/MLOps lead to architect end-to-end ML platforms and deploy models at enterprise scale. You will drive CI/CD with Azure Pipelines, MLflow, and Databricks, build governance, monitoring, and feature-registry strategies, and mentor other engineers.

You should have 5–8+ years in ML engineering, strong Spark, Databricks, Kubernetes, and Docker expertise, and the ability to operate across hybrid/multi-cloud environments.

Qualifications

• 5–8+ years in ML Engineering, MLOps, or high-scale ML systems.
• Proven track record deploying ML at enterprise scale with audit/monitoring layers.
• Familiarity with hybrid/multi-cloud infrastructure.

Responsibilities

• Architect end-to-end ML infrastructure across pipelines, serving, monitoring, and governance.
• Lead deployment of high-impact models (forecasting engines, NLP models).
• Design 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.
• Location: CDMX

Skills

ML Engineering

MLOps

Spark

Kubernetes

Docker

Tools

Azure Databricks

MLflow

Kubernetes

Docker

Azure Pipelines

Job description

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

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.
• Location: CDMX

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

• Commitment to Professional Development
• Flexible and Collaborative Culture
• Total Rewards
• Specific benefits are determined by the employment type and location.