Data Scientist - ML Engineering

We are:

impact.

With the right people and the right ideas, there’s no limit to what we can achieve

Are you a fit?

Key Responsibilities

• 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.

Must-have Skills

• 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.

Nice-to-have:

• 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.

What we offer:

• A High-Impact Environment
• Commitment to Professional Development
• Flexible and Collaborative Culture
• Global Opportunities
• Vibrant Community
• Total Rewards