Production ML & GenAI Lead - Azure Databricks

Socket.dev; Birmingham (AL); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Health insurance

401(k) with Company matching

Paid time off

Job summary

Protective Life is accelerating its AI/ML initiatives with a hands-on technical leader who will own the path from experimentation to governed production for machine learning and GenAI on our Databricks Lakehouse on Microsoft Azure.

You will mentor ML and data engineers, set engineering standards for the ML lifecycle, and collaborate with product managers, Model Risk, and Security to ensure reliable, monitored, and compliant models.

Qualifications

• 8+ years in software, data, or ML engineering with production ML systems.
• Demonstrated technical leadership and mentoring; setting standards for non-trivial systems.
• Strong Python and SQL with end-to-end ML lifecycle experience and common frameworks (scikit-learn, PyTorch, TensorFlow).
• Hands-on MLOps experience with MLflow and Azure Databricks strongly preferred.
• Experience delivering GenAI/LLM applications (RAG, embeddings, prompt design, evaluation).
• Knowledge of modern data stack (dltHub, dbt, Dagster) on Databricks lakehouse.

Responsibilities

• Lead the design and delivery of production ML and GenAI systems on Azure Databricks from data sourcing to deployment and retraining.
• Set technical direction and standards for the ML lifecycle, including experimentation, feature engineering, training, evaluation, deployment, drift detection.
• Provide hands-on leadership and mentoring to ML and data engineers through reviews and pairing.
• Build and operate MLOps foundations using MLflow, Databricks Model Serving, and Unity Catalog.
• Architect GenAI capabilities with RAG, embeddings, vector search, and governance.
• Partner with Model Risk, Data Governance, Legal, and Security to meet regulatory requirements and ensure explainability.

Skills

Python

SQL

ML lifecycle

Mentoring & leadership

GenAI / LLM

Education

Bachelor's degree in Computer Science / Data Science / Statistics / Engineering

Tools

Databricks

MLflow

Azure DevOps

Unity Catalog

Dagster

Job description

Protective Life is accelerating its AI/ML initiatives with a hands-on technical leader who will own the path from experimentation to governed production for machine learning and GenAI on our Databricks Lakehouse on Microsoft Azure.

You will mentor ML and data engineers, set engineering standards for the ML lifecycle, and collaborate with product managers, Model Risk, and Security to ensure reliable, monitored, and compliant models.