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

Protective Life; Birmingham (AL); On-site; USD 125,000 - 180,000; Full time

Benefits offered by this job

Health insurance

401(k) with company match

Paid time off

Job summary

Protective Life seeks an experienced AI/ML Engineering Lead to own the path from experiment to governed production for ML/GenAI on Azure Databricks. You will set engineering standards for the ML lifecycle, mentor ML and data engineers, and deliver critical components while collaborating with product managers, data engineers, and Model Risk partners.

As a regulated life insurer, you will ensure model reliability, monitoring, documentation, fairness, and explainability, and guide pragmatic

Qualifications

• 8+ years in software, data, or ML engineering with production ML experience.
• Strong leadership in mentoring and setting standards (not required to manage people).
• Proficient Python and SQL; experience with scikit-learn, PyTorch, or TensorFlow.
• Hands‑on MLOps with MLflow and Azure Databricks preferred.
• Experience delivering GenAI/LLM apps: RAG, embeddings, vector DBs, prompt/system design.

Responsibilities

• Lead design and delivery of production ML/GenAI systems on Azure Databricks from data sourcing to deployment and retraining.
• Set standards for ML lifecycle including experimentation, training, evaluation, deployment, drift detection, retraining.
• Provide hands‑on technical leadership through design reviews and pairing; raise engineering craft bar.
• Operate MLOps foundations using MLflow, Databricks Model Serving, and Unity Catalog.

Skills

Python

SQL

ML lifecycle

MLOps

Code reviews

Education

Bachelor's degree in CS/DS/Engineering

Tools

Azure Databricks

MLflow

Unity Catalog

Dagster

dbt

Job description

Protective Life seeks an experienced AI/ML Engineering Lead to own the path from experiment to governed production for ML/GenAI on Azure Databricks. You will set engineering standards for the ML lifecycle, mentor ML and data engineers, and deliver critical components while collaborating with product managers, data engineers, and Model Risk partners.

As a regulated life insurer, you will ensure model reliability, monitoring, documentation, fairness, and explainability, and guide pragmatic