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 Required
- 5-8+ years of experience in ML Engineering, MLOps, or high-scale ML systems
- Deep expertise in Spark, Azure Databricks, MLflow, Kubernetes, and Docker
- Proven experience deploying machine learning systems at enterprise scale with audit and monitoring layers
- Familiarity with hybrid or multi-cloud infrastructure
- AI tooling proficiency for drafting, analysis, research, or process automation
- Leadership experience in ML platform or DevOps teams
- Experience with feature stores and feature engineering
- AutoML experience
- H2O experience
Wizeline Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Wizeline and has not been reviewed or approved by Wizeline.
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Healthcare Strength — Core coverage spans medical, dental, vision, life and disability insurance, with private medical commonly included in Mexico. Additional support such as EAP and wellness programs is also referenced.
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Leave & Time Off Breadth — Paid vacation, holidays, sick leave, volunteer time, and parental leave are called out, alongside flexible or remote work options. Unlimited PTO is mentioned in some contexts.
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Retirement Support — Savings and retirement elements appear across regions, including a 401(k) in the U.S. and a savings fund and profit sharing in Mexico. These components provide structured long-term financial benefits.
Wizeline Insights
What We Do
Wizeline, a global technology services provider, builds the best digital products and platforms at scale. We focus on measurable outcomes, partnering with our customers to modernize core technologies, mature data-driven capabilities, and improve user experience.
