Hiring: AI/ML Engineering Lead Company: Protective Life Location: United States Job Posted Time: 2026-09-15 10:21:46 Target Skills & Keywords : Azure, Azure DevOps, CI/CD, Dagster, Databricks, Delta Lake, Feature Store, Git, LLM, MLOps, MLflow, Machine Learning, Model Registry, PyTorch, Python, RAG, SQL, TensorFlow, dbt, scikit-learn About the job Experience: •8+ years in software, data, or ML engineering, including several years building and operating production ML systems Required Skills: •Lead the design and delivery of production ML and GenAI systems on Azure Databricks — from problem framing and data sourcing through deployment, monitoring, and retraining •Set technical direction and standards for the ML lifecycle — experimentation, feature engineering, training, evaluation, deployment, drift detection, and retraining — and hold the team to them •Provide hands-on technical leadership and mentoring to ML and data engineers through design and code reviews, pairing, and raising the bar on engineering craft •Build and operate MLOps foundations using MLflow (experiment tracking, model registry), Databricks Model Serving, and Unity Catalog for governed feature and model management •Architect GenAI capabilities — retrieval-augmented generation (RAG), embeddings and vector search, prompt/system design, evaluation harnesses, guardrails, and human-in-the-loop review •Depend on the pod's data stack — dlt (dltHub) ingestion, dbt models, and Dagster orchestration — to ensure training data and features are reliable, versioned, and reproducible •Establish CI/CD for ML in Azure DevOps (ADO) — automated testing, model packaging, and repeatable, auditable deployments across environments •Own model performance and cost — monitoring accuracy and output quality, latency, and drift, and managing training/serving compute with a FinOps mindset Qualifications: •Demonstrated technical leadership — mentoring engineers, setting standards, and leading the design of non-trivial systems (formal people management not required, but valued) •Strong Python and SQL, with deep experience across the end-to-end ML lifecycle and common ML frameworks (e.g., scikit-learn, PyTorch, or TensorFlow) •Hands-on MLOps experience — experiment tracking, model registry, deployment/serving, monitoring, and retraining — with MLflow and Azure Databricks strongly preferred •CI/CD experience with Azure DevOps (ADO) and Git-based, test-supported development practices •Solid functional working knowledge of Microsoft Azure — compute, storage, identity, and Azure AI/OpenAI services •Demonstrated rigor in documentation, model evaluation, and secure, compliant handling of sensitive data •Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, or a related field — or equivalent practical experience •Operational familiarity with Databricks Mosaic AI, Feature Store / Unity Catalog features, or Vector Search, and with Azure Machine Learning Compensation: •$124,500 - $180,000 / year Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!