AI/ML Engineering Lead

Protective•Birmingham, NE

1d•$124,500 - $180,000•Onsite

About The Position

The AI/ML Engineering Lead is a hands-on technical leader who owns the path from experiment to governed production for machine learning and GenAI on our Databricks Lakehouse on Microsoft Azure. You will set the engineering standards for the ML lifecycle, mentor ML and data engineers, and personally deliver critical components — while working closely with product managers, data engineers, and Model Risk partners. As a regulated life insurer, we hold models to disciplined standards: this role is accountable not only for shipping models but for their reliability, monitoring, documentation, fairness, and explainability.

Requirements

8+ years in software, data, or ML engineering, including several years building and operating production ML systems.
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.
Experience delivering GenAI/LLM applications: RAG, embeddings and vector databases, prompt/system design, and structured evaluation.
Experience with the modern data stack the pod uses — 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, 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.

Nice To Haves

Experience in financial services or insurance ML — underwriting, actuarial, fraud, claims, or customer models — and 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, and with Azure Machine Learning.
Experience applying responsible-AI and model-governance techniques — bias/fairness testing and explainability (e.g., 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 — 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.
Partner with Model Risk, Data Governance, Legal, and Security so models meet documentation, validation, explainability, bias/fairness, and privacy expectations.
Translate product outcomes into ML solutions with product managers — balancing discovery experimentation against production reliability and time-to-value.
Contribute to AI governance — model inventory, documentation, approval workflows, and responsible-AI practices aligned to company and regulatory expectations.
Guide pragmatic adoption of the applied-AI landscape appropriate to a mid-sized carrier, avoiding hype and over-engineering.