AI/ML Engineering Lead

What you'll do

• Lead ML and GenAI systems from problem framing and data sourcing through deployment, monitoring, and retraining; set ML lifecycle engineering standards.
• Build MLOps and GenAI capabilities, including model serving, RAG, evaluation, guardrails, and CI/CD; monitor model performance, cost, and drift.
• Mentor engineers and partner with product, Model Risk, governance, legal, and security teams on reliable, documented, fair, and compliant models.

What they ask for

• 8+ years in software, data, or ML engineering, including several years building and operating production ML systems.
• Technical leadership experience, including mentoring engineers, setting standards, and leading non-trivial system design.
• Strong Python and SQL; end-to-end ML lifecycle experience and common ML frameworks.
• Experience delivering GenAI/LLM applications, modern data stack and Databricks lakehouse, Azure DevOps CI/CD, and Microsoft Azure.

Nice to have

• Financial services or insurance ML experience and familiarity with model risk management.
• Familiarity with Databricks Mosaic AI, Feature Store / Unity Catalog features, Vector Search, or Azure Machine Learning.

Pay

USD 124,500–180,000 per-year-salary (base salary)

Tools

Python, SQL, MLflow, Azure Databricks, dlt (dltHub), dbt, Dagster, Azure DevOps (ADO)

AI focus

Build and operate production ML and GenAI/LLM systems, including RAG, embeddings, vector search, evaluation, guardrails, monitoring, and responsible-AI governance.