Head of AI and Automation Engineering - Commercial & Securities

This role will report onsite to our San Diego, CA. location. Remote or hybrid is not available.

Axos Bank is seeking a Head of AI and Automation Engineering – Commercial & Securities group. This exciting, new role exists to make Operational Transformation's mission real: using AI and automation to drive operational efficiency across Commercial Lending, Commercial Banking & Securities. It is the platform and stack architect for that effort, owning the technical backbone every delivery pod builds on, including ReTool, n8n, JavaScript and/or Python, the AI/intelligence agent layer, the operational data store (ODS), and the API layer, while setting the standards and patterns that keep five-plus pods building the same way instead of reinventing the stack independently.

This is a hands-on technical leadership role, not a coordination or project management seat. The Head of AI and Automation Engineering needs genuine depth in AI technology, including working knowledge of what these tools can automate reliably today and where they still need guardrails, and personally drives technical execution rather than only setting direction. They are the standing platform expert every pod's Forward Engineers and Platform Engineers turn to when a design question needs a definitive answer. Forward Engineering and Platform Engineering report into this role across all pods, giving it direct control over how the technical layer gets built, not just influence over it.

Responsibilities:

Platform & Stack Architecture

• Own the technical architecture of the shared platform stack: ReTool (front-end/UI layer), n8n (workflow/automation layer), the AI/intelligence agent layer, the ODS, and the API layer that connects them.
• Set and enforce engineering standards and design patterns across all pods, including schema conventions, API contracts, reusable n8n node patterns, and ReTool component standards, so pods compose on a common foundation instead of diverging.
• Own the ODS data model and the API layer design decisions that every pod depends on.
• Ensure compliance with all engineering and information security policies and directives.

Operational Efficiency Through AI & Automation

• Own the design and governance of the AI/intelligence agent layer: where agentic automation is reliable enough for production use versus where it still needs guardrails, and the standards pods follow when building new agents.
• Implement an AI-driven development lifecycle (AIDLC) across Operational Transformation, embedding AI into planning, coding, testing, and delivery as the standard way pods work.
• Translate the transformation's core goal, using AI and automation to drive operational efficiency, into concrete technical execution.
• Ensure AI-enabled automation across all pods is designed and executed to a consistent technical standard.

Technical Execution & Delivery Leadership

• Personally drive technical execution across pods. This is a roll-up-your-sleeves role, requiring the leader to step directly into a pod's hardest technical problems.
• Lead, develop, and hold accountable the Forward Engineers and Platform Engineers across all pods, including technical career growth and consistent technical practice.
• Partner with each pod's Process Product Management function to translate business-process findings into sound technical design before a pod starts building.
• Serve as the technical escalation point across Commercial Lending, Commercial Banking & Securities when a design or architecture decision needs a single accountable owner.
• Establish technical testability and automation standards that Quality Assurance tests against, ensuring QA in each pod has a consistent, automatable standard to validate rather than a bespoke one per pod.
• Own pod-level technical delivery pace and quality, exercised through the Forward Engineers and Platform Engineers embedded in each pod rather than through direct day-to-day pod management.
• Maintain cross-pod technical coherence, flagging and correcting pattern drift before it hardens into incompatible platform forks across Servicing, Portfolio Management/Special Assets, Securities, and Commercial Banking.

Requirements:

• Bachelor's degree or higher in related field
• 7+ years in software engineering with meaningful time in a hands-on architecture or platform-lead capacity.
• Experience with ReTool and n8n “or equivalent” (Appsmith, Power Apps, Temporal, Airflow, Zapier and similar)
• Direct experience designing and operating API layers and operational data stores (ODS) that multiple teams build against.
• Current working knowledge of AI/LLM technology.
• Proven experience shipping AI/agentic automation into production.
• Experience setting engineering standards across multiple teams.

Preferred:

• Track record implementing AI-driven development practices (AIDLC) at scale, with measurable delivery impact.
• Financial services or other regulated-industry experience strongly preferred, given the audit and compliance context surrounding ODS, API, and AI-driven changes.