AI/ML Engineering Lead

KEY 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.

QUALIFICATIONS

REQUIRED QUALIFICATIONS

• 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.

PREFERRED QUALIFICATIONS

• 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.