Data Scientist - ML Engineering
Summary
Wizeline is hiring a Data Scientist focused on ML Engineering in Mexico City to architect end-to-end ML infrastructure — pipelines, serving, monitoring, and governance — and deploy enterprise-scale models like forecasting, optimization, and NLP systems. Core stack: Spark, Azure Databricks, MLflow, Kubernetes, Docker, and Azure Pipelines for CI/CD.
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.
Skills
As published by greenhouse · 11 questions · 4 written answers
Basics
First Name, Last Name, Email, Phone, Resume/CV, Location
Short answers (2)
- LinkedIn Profile
- Website optional
Pick from a list (5)
- I have read and accept Wizeline's
- Have you been referred to by any staff member of Wizeline?
- Do you have any family relationship, economic and/or personal relationship with any staff member, Board member or shareholder of Wizeline?
- Do you have any family and/or contractual relationship with a direct competitor of Wizeline?
- Do you have any relationship with any government entity and/or a government official?
Written answers (4)
- If your answer is YES, please provide the name and position of the referring staff member:
- If your answer is YES, please provide the name, position of the staff member, officer, board member, and/or shareholder(s), specifying the type of relationship:
- If your answer is YES, please provide the name of the competitor(s), specifying the type of relationship:
- If your answer is YES, please provide the name of the entity and/or government official, specifying the type of relationship: