Data Scientist- ML Engineering

Wizeline

3 months ago

Mexico City, Mexico

Senior / Staff+

H1B Sponsor

Responsibilities

Architect end-to-end ML infrastructure, including pipelines, model serving, monitoring, and governance.
Lead deployment of high-impact ML solutions such as forecasting engines, optimization models, and NLP use cases.
Design and manage advanced CI/CD workflows using Azure Pipelines, MLflow, and Databricks.
Implement model registry, versioning, lineage, and audit-compliant governance frameworks.
Build and maintain monitoring systems to detect model drift and automate retraining cycles.
Mentor MLOps engineers and collaborate with platform, data, and product teams to ensure seamless integration.
Drive adoption of MLOps best practices across containerization, observability, testing, and scalable infrastructure.

5–8+ years of experience in ML Engineering, MLOps, or building large-scale ML systems.
Strong expertise with Spark, Azure Databricks, MLflow, Kubernetes, and Docker.
Proven track record deploying ML solutions at enterprise scale with audit, governance, and monitoring layers.
Experience designing ML infrastructure and CI/CD pipelines in cloud environments.
Knowledge of hybrid or multi-cloud architectures.
Bachelor’s degree required; Master’s preferred in Computer Science, Engineering, or related fields.

Apache SparkAzureDatabricksDockerKubernetesMLflow