Data Scientist - ML Engineering
Wizeline Open · verified Oct 22, 2026 At a glance
Mid-level AI Research role at Wizeline. Ciudad de Mexico · full-time.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Design ML infrastructure spanning data pipelines, model serving, monitoring and governance
- Deploy forecasting, optimization and language models, then build systems to track drift and trigger retraining
- Create CI/CD workflows with Azure Pipelines, MLflow and Databricks, including model records, version history and lineage
- Guide MLOps engineers and connect platforms across teams while promoting containerization and observability practices
What you bring
- At least five years in ML engineering, MLOps or large-scale machine learning systems
- Strong hands-on knowledge of Spark, Azure Databricks, MLflow, Kubernetes and Docker
- A record of deploying enterprise ML with audit and monitoring controls, plus experience with hybrid or multi-cloud setups
Who this fits
This mid-level role suits someone with substantial ML engineering or MLOps experience who can shape platform practices and mentor engineers. The position is based in Ciudad de Mexico, Mexico.
From the employer
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.