10 hours ago
Mexico City, MexicoSenior / Staff+
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.
Tech Stack
Categories
About Wizeline
Wizeline is a global technology services firm that designs and builds digital products and platforms for enterprises, offering product development, UX, cloud/DevOps, data, and AI services. It works on a consulting and managed-services model to modernize core systems and deliver measurable outcomes. Founded in 2014 and headquartered in San Francisco, it operates internationally and is privately held.