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Data Scientist - ML Engineering

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Data Scientist - ML Engineering in Others

  • Wizeline
  • Full time
  • Email
  • Mexico City, Mexico

Responsibilities

  • Architect end-to-end ML infrastructure across pipelines, serving, monitoring, and governance.
  • Lead deployment of forecasting engines, optimization solvers, and NLP models.
  • Design CI/CD workflows using Azure Pipelines, MLflow, and Databricks.
  • Implement model registry, versioning, lineage, and audit compliance capabilities.
  • Build model-drift monitoring systems and retraining automation.
  • Mentor MLOps engineers and guide cross-functional platform integration.
  • Drive adoption of MLOps practices involving containerization and observability.

Requirements

  • Require 5–8+ years of experience in ML Engineering, MLOps, or high-scale ML systems.
  • Require deep expertise in Spark, Azure Databricks, MLflow, Kubernetes, and Docker.
  • Require a proven track record deploying ML at enterprise scale with audit and monitoring layers.
  • Require familiarity with hybrid and multi-cloud infrastructure.
  • Prefer leadership experience in ML platform or DevOps teams.
  • Prefer experience with feature stores and feature engineering; AutoML and H2O are pluses.
  • Expect proficiency with AI tools to improve drafting, analysis, research, or process automation and to recommend effective AI use.

Benefits

  • High-impact environment.
  • Commitment to professional development.
  • Flexible and collaborative culture.
  • Global opportunities.
  • Vibrant community.
  • Total rewards.
  • Specific benefits depend on employment type and location.

Wizeline

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.

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