Data Scientist - ML Engineering
Ciudad de Mexico
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full Time
From the employer’s posting
What you’ll work on
Full postingArchitect end-to-end ML infrastructure across pipelines, serving, monitoring, and governance
Lead deployment of high-impact models such as forecasting, optimization, and NLP
Design CI/CD workflows using Azure Pipelines, MLflow, and Databricks
Build monitoring for model drift and retraining automation
Mentor MLOps engineers and guide cross-functional platform integration
From the employer’s posting
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.
Build monitoring systems for model drift and retraining automation.
Mentor MLOps engineers and guide cross-functional platform integration.
What you’ll bring
All qualificationsCore experience
- 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
Qualification wording
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.
Tools in this posting
- Azure
- Databricks
- Docker
- Kubernetes
- MLflow
- Spark
Source — Tool mentions in context
- 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.
- 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.
Posting record
Report an errorOpen 6 monthsWe first saw this posting 190 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Apr 22, 2026 · 190 days ago
- Recorded sightings
- 107
- Last seen by us
- Oct 29, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
Complete your application on www.wizeline.ai. The employer’s form will show what is required.
Already applied? Track this application
Job 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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Ciudad de Mexico
- Proven track record deploying ML at enterprise scale with audit and monitoring layers. - Familiarity with hybrid/multi-cloud infrastructure. Nice-to-have:
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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