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.
Versátil and collaborative culture.
Global opportunities.
Vibrant community.
Total rewards.
Specific benefits depend on employment type and location.

📌 Data Scientist - Ml Engineering (Xico)
🏢 Wizeline
📍 Xico