Technical Program Manager - AI Transformation

Headquartered in San Diego, Mulligan Funding serves as a leading provider of working capital (Up to $5M) to the small and medium-sized businesses that fuel our country. Since 2008, we have prided ourselves on our collaborative, innovative, and customer-focused approach. Enjoying a period of unprecedented growth, driven by the combination of cutting‑edge technology, human touch, and unwavering integrity, we are looking to add to our people‑first culture, with highly motivated and results‑oriented professionals, to push the limits of what’s possible while creating value for all of our partners.

We have embarked on the journey to transform our small business lending platform and product through the thoughtful application of automation, data, and AI. This role will be part of the core AI group responsible for shaping and executing a multi‑year vision to modernize core lending workflows — including pre‑underwriting, underwriting, and sales — with the goal of driving operating efficiency, improving unit economics, and fundamentally upgrading the customer experience for small businesses.

Establishment of an AI‑first approach, using automation and modern tooling to improve efficiency and scale is a key enabler in our mission to provide affordable access to credit to small businesses.

We are seeking a Technical Project Manager (TPM) to serve as the execution lead within our AI Transformation Pod. This role will partner closely with the AI Program Lead and engineering team to convert AI‑driven product vision into structured, production‑ready delivery plans.

The TPM will own end‑to‑end execution of AI‑enabled initiatives across pre‑underwriting, underwriting, sales, and all other workflows. The role requires strong technical fluency, systems thinking, and the ability to operate effectively in a regulated lending environment. This is not a traditional project coordination role. The TPM will play a critical role in shaping technical design decisions, sequencing complex dependencies, and ensuring AI capabilities are deployed safely, efficiently, and at scale.

• Translate AI‑driven business opportunities into detailed technical execution plans
• Break initiatives into phased milestones aligned with product, engineering, and risk requirements.
• Define clear scope, dependencies, timelines, and success metrics.
• Identify required vendor integrations and cross‑functional inputs.

• Partner with engineers to define system architecture, orchestration patterns, and integration approaches.
• Coordinate dependencies with Data Engineering, Data Science, and Salesforce teams.
• Ensure solutions integrate cleanly with Salesforce workflows and case management, Azure
  
  ML‑deployed models and external vendors

• Establish release plans and rollout strategies.
• Ensure appropriate logging, auditability, and version control are in place.
• Maintain risk and dependency tracking across initiatives.
• Define fallback paths and exception‑handling mechanisms before deployment.

• Coordinate model deployment cycles with Azure
  
  ML and applied AI engineers.
• Ensure evaluation, monitoring, and performance tracking frameworks are defined.
• Align AI outputs with business‑defined confidence thresholds and routing logic.

• Serve as the primary execution liaison between AI Program Lead, engineering, operations, data teams and all other stakeholders involved in AI initiatives
• Communicate progress, risks, and trade‑offs clearly to leadership.

• 5–10 years of experience in Technical Program Management, Engineering Program Management, or related roles.
• Bachelor’s degree in Computer Science, Engineering, or related technical field
• Master’s degree in a quantitative or technical discipline is strongly preferred.
• Demonstrated experience leading delivery of AI, machine learning, or data‑driven systems in production environments
• Strong understanding of API integrations, Workflow orchestration systems, Salesforce or similar CRM/workflow platforms and Cloud environments (Azure preferred)
• Experience coordinating model deployments or ML‑enabled systems is strongly…