Lead MLOps Engineer

About the Role:
We are investing heavily in growing our Data Science and Machine Learning capabilities across underwriting, claims, and customer experience. The Lead MLOps Engineer is a net new role designed to help scale our AI/ML operations function. You’ll play a pivotal part in designing and building the foundation for MLOps within the organization while partnering with stakeholders across the business.

Key Responsibilities:
• Build and maintain end-to-end MLOps pipelines encompassing model development, deployment, monitoring, and lifecycle management.
• Define and implement CI/CD workflows for ML models, ensuring versioning, reproducibility, and scalability.
• Establish frameworks and reusable tools that empower data scientists and developers to deploy and monitor models efficiently.
• Develop and enforce governance frameworks supporting model explainability, ethical AI practices, and compliance.
• Collaborate with cross-functional stakeholders to align technical solutions with business needs.
• Contribute to the design of LLMOps capabilities as part of our forward-looking AI strategy.
• Provide technical mentorship and help shape future MLOps team growth.

Minimum Qualifications:
• 2+ years of hands-on MLOps experience, with additional experience as a data scientist or software engineer considered.
• Expertise with:
o Python (including libraries such as Pandas, Polars, PySpark, TensorFlow, PyTorch)
o SQL and DataFrame-based processing workflows
o ML lifecycle tools such as MLflow, Data Bundles, Unity Catalog
o Code development environments including VSCode
o CI/CD pipelines using tools such as GitHub Actions or similar
• Familiarity with monitoring frameworks and observability concepts for ML systems.
• Strong understanding of governance principles including model versioning, reproducibility, explainable AI, and ethical AI practices.
• Demonstrated ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders.
• Proven ability to collaborate across teams in a structured, transparent manner.

Preferred Qualifications:
• Experience supporting property and casualty insurance business use cases.
• Familiarity with Databricks.
• Exposure to LLMOps concepts and tooling.

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