Data Scientist - ML Engineering

Wizeline·Ciudad de Mexicosenior

In short

→Científico de datos enfocado en ingeniería de ML y MLOps.
→Diseña y despliega infraestructura de ML a escala empresarial con monitoreo y compliance.
→Destacado por liderar la adopción de mejores prácticas en CI/CD y observabilidad en Azure.

Fluency in English is required for collaboration across global teams.

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The questions they'll ask you

1. ¿Cómo has implementado rastreo de línea y versión de modelos en un entorno de producción usando MLflow?

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💵 USD · Remote · No visa

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What they ask for

✓5 a 8+ años en ingeniería de ML o MLOps.
✓Experto en Spark, Azure Databricks, MLflow, Kubernetes y Docker.
✓Experiencia demostrada desplegando modelos a escala empresarial con auditoría.
✓Capacidad para implementar monitoreo de drift y automatización de reentrenamiento.
✓Habilidad para guiar y mentorizar a equipos de MLOps.
✓Conocimiento en infraestructura híbrida/multi-nube.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

SparkAzure DatabricksMLflowKubernetesDockerAzure PipelinesCI/CDModel RegistryVersioningLineage

Who should you write to at Wizeline?

Your free dossier identifies the people who'd interview you — their background, what they value, and how to reach out so you stand out before applying.

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 .

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