AI/ML Engineering Lead — GenAI, MLOps on Azure

Protective; Nebraska; Hybrid; USD 125,000 - 180,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

401(k) with company match

Paid time off

Job summary

Protective is transforming its software approach to a product-led model, focusing on ML/GenAI within the Databricks Lakehouse on Azure. The AI/ML Engineering Lead role drives the end-to-end ML lifecycle, from experimentation to governed production, mentoring teammates and aligning with product and risk teams.

You will own MLOps foundations, ensure reliability, monitor costs, and collaborate with Model Risk, Data Governance, and Security to meet regulatory expectations.

Qualifications

• 8+ years in software, data, or ML engineering and production ML systems experience.
• Strong technical leadership and mentoring experience.
• Deep Python and SQL skills with end-to-end ML lifecycle knowledge.
• Hands-on MLOps experience with MLflow and Azure Databricks preferred.
• Experience delivering GenAI/LLM apps: RAG, embeddings, vector databases.

Responsibilities

• Lead design and delivery of production ML and GenAI systems on Azure Databricks.
• Set ML lifecycle standards and ensure governance and quality.
• Provide hands-on leadership and mentoring to ML and data engineers.
• Build and operate MLOps foundations (MLflow, model registry, serving).
• Architect GenAI capabilities including RAG, embeddings, prompt design, evaluation.

Skills

Python

SQL

ML lifecycle

Mentoring

Education

Bachelor's degree

Tools

Azure Databricks

MLflow

Databricks Model Serving

Unity Catalog

Dagster

Job description

Protective is transforming its software approach to a product-led model, focusing on ML/GenAI within the Databricks Lakehouse on Azure. The AI/ML Engineering Lead role drives the end-to-end ML lifecycle, from experimentation to governed production, mentoring teammates and aligning with product and risk teams.

You will own MLOps foundations, ensure reliability, monitor costs, and collaborate with Model Risk, Data Governance, and Security to meet regulatory expectations.