AI/ML Engineering Lead: GenAI & Production ML on Azure

Protective Life; United States; On-site; USD 125,000 - 180,000; Full time

Benefits offered by this job

Health insurance

Dental and vision insurance

401(k) with company match

Pension plan

Paid time off

Mental health benefits

Employee assistance program

Job summary

Protective Life is seeking an AI/ML Engineering Lead to own production ML and GenAI components on Azure Databricks. You will set engineering standards, mentor engineers, and deliver critical modules while collaborating with product managers, data engineers, and Model Risk partners.

This role emphasizes reliability, monitoring, explainability, and governance in a regulated insurer context with a focus on responsible AI and cross-functional collaboration.

Qualifications

• 8+ years in software, data, or ML engineering with production ML experience.
• Strong Python and SQL across ML lifecycle; familiar with major frameworks.
• Hands-on MLOps: tracking, model registry, deployment, monitoring, retraining.
• Experience delivering GenAI/LLM apps and using Azure Databricks and Delta Lake.

Responsibilities

• Lead production ML and GenAI systems on Azure Databricks from data sourcing to deployment.
• Set standards for ML lifecycle and ensure compliance and explainability.
• Provide hands-on leadership through design reviews and code reviews.
• Build MLOps foundations using MLflow, Model Serving, and Unity Catalog.
• Govern GenAI capabilities with RAG, embeddings, prompt design, and evaluation.

Skills

Python

SQL

ML engineering

Mentoring

MLOps

CI/CD

Education

Bachelor's degree in CS/Data Science/Engineering

Tools

MLflow

Azure Databricks

Unity Catalog

Dagster

Azure DevOps

Git

Job description

Protective Life is seeking an AI/ML Engineering Lead to own production ML and GenAI components on Azure Databricks. You will set engineering standards, mentor engineers, and deliver critical modules while collaborating with product managers, data engineers, and Model Risk partners.

This role emphasizes reliability, monitoring, explainability, and governance in a regulated insurer context with a focus on responsible AI and cross-functional collaboration.