AI/ML Engineering Lead

Protective•Nebraska - Virtual, NE

1d•Onsite

About The Position

Protective Life is transforming its software development and operations by adopting a product operating model with empowered, outcome-oriented teams. The company is investing in machine learning and generative AI to enhance customer service and business operations. The AI/ML Engineering Lead will be part of the Voyager product pod, which covers Life, Annuities, and Employee Benefits lines. This role is a hands-on technical leadership position responsible for managing the entire lifecycle of machine learning and GenAI systems, from experimentation to production deployment, on a Databricks Lakehouse environment hosted on Microsoft Azure. The lead will establish engineering standards for the ML lifecycle, mentor ML and data engineers, and contribute directly to critical components. Collaboration with product managers, data engineers, and Model Risk partners is essential. As a regulated entity, the role requires adherence to disciplined standards for model reliability, monitoring, documentation, fairness, and explainability.

Requirements

8+ years in software, data, or ML engineering, with significant experience building and operating production ML systems.
Demonstrated technical leadership, including mentoring engineers, setting standards, and leading the design of complex systems.
Strong proficiency in Python and SQL, with deep experience across the end-to-end ML lifecycle and common ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
Hands-on MLOps experience (experiment tracking, model registry, deployment/serving, monitoring, retraining) with MLflow and Azure Databricks strongly preferred.
Experience delivering GenAI/LLM applications: RAG, embeddings and vector databases, prompt/system design, and structured evaluation.
Experience with dlt (dltHub) ingestion, dbt modeling, and Dagster orchestration on a Databricks lakehouse (Delta Lake).
CI/CD experience with Azure DevOps (ADO) and Git-based, test-supported development practices.
Working knowledge of Microsoft Azure (compute, storage, identity, 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.

Nice To Haves

Experience in financial services or insurance ML (underwriting, actuarial, fraud, claims, or customer models).
Familiarity with model risk management practices (e.g., SR 11-7-aligned validation).
Familiarity with Databricks Mosaic AI, Feature Store / Unity Catalog features, or Vector Search.
Familiarity with Azure Machine Learning.
Experience applying responsible-AI and model-governance techniques (bias/fairness testing, explainability like SHAP, LIME).
Experience with streaming or real-time inference and low-latency serving.
Experience coaching or formally managing engineers.
Advanced degree in a quantitative field.
Relevant certification such as Databricks Certified Machine Learning Engineer or Microsoft Azure AI Engineer Associate.

Responsibilities

Lead the design and delivery of production ML and GenAI systems on Azure Databricks, covering problem framing, data sourcing, deployment, monitoring, and retraining.
Set technical direction and standards for the ML lifecycle, including experimentation, feature engineering, training, evaluation, deployment, drift detection, and retraining.
Provide hands-on technical leadership and mentoring to ML and data engineers through design and code reviews, pairing, and raising engineering standards.
Build and operate MLOps foundations using MLflow, Databricks Model Serving, and Unity Catalog for governed feature and model management.
Architect GenAI capabilities, including retrieval-augmented generation (RAG), embeddings and vector search, prompt/system design, evaluation harnesses, guardrails, and human-in-the-loop review.
Utilize the pod's data stack (dlt, dbt, Dagster) to ensure reliable, versioned, and reproducible training data and features.
Establish CI/CD for ML in Azure DevOps (ADO), including automated testing, model packaging, and repeatable, auditable deployments across environments.
Own model performance and cost, monitoring accuracy, output quality, latency, and drift, and managing compute resources with a FinOps mindset.
Partner with Model Risk, Data Governance, Legal, and Security to ensure models meet documentation, validation, explainability, bias/fairness, and privacy expectations.
Translate product outcomes into ML solutions with product managers, balancing experimentation with production reliability and time-to-value.
Contribute to AI governance, including model inventory, documentation, approval workflows, and responsible-AI practices aligned with company and regulatory expectations.
Guide the pragmatic adoption of applied AI technologies suitable for a mid-sized carrier, avoiding hype and over-engineering.