Data Scientist - ML Engineering

1 нед. назад

МексикаSenior

sparkazure databricksmlflowkubernetesdockerci/cdcontainerizationmonitoring

Lead ML engineering for end-to-end infrastructure, deployment, and MLOps best practices at .

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.

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.

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.

, 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
Sounds awesome, right? Now, let’s make sure you’re a good fit for the role: