Principal AI Deployment Manager, Controller

Overview

This role is a Principal AI Deployment Manager, Controller focused on implementing AI solutions for finance teams with controller-level accounting depth. You will partner with finance leaders and subject-matter experts to understand current processes and systems, identify practical opportunities for improvement, and define an evaluated workflow from discovery through delivery and adoption.

Work will center on order-to-cash (O2C), month-end accounting close, and procure-to-pay (P2P). You will translate accounting policies, controls, system behavior, data requirements, and exceptions into clear requirements, then collaborate with engineering to design and deliver a finance workflow that can be tested and reviewed.

Responsibilities

• Lead finance process discovery

  • Run working sessions with controllers, accounting teams, and other client SMEs to map O2C, close, or P2P workflows, including systems, controls, handoffs, and exceptions.
  • Establish the current-state baseline and define what success means to the finance team.
  • Assess process fit, data readiness, risk, and where human review or approval must remain.

• Translate controller needs into product requirements

  • Convert accounting workflows and policy requirements into structured product requirements, decision rules, acceptance criteria, product roadmap, and test cases.
  • Prioritize opportunities by feasibility, finance impact, data quality, and implementation effort.
  • Provide clear recommendations on where AI is useful, where standard automation is sufficient, and where processes are not ready.

• Partner on delivery with engineering

  • Work closely with Forward Deployed Engineers to shape an MVP plan, clarify dependencies, and make tradeoffs across scope, quality, and timing.
  • Review workflow behavior against representative finance cases, including edge conditions, exceptions, reconciliations, and audit evidence.
  • Keep model limitations, data quality, access controls, and production-readiness risks visible as work moves from prototype to deployment.

• Measure outcomes and guide adoption

  • Define evaluation criteria and business outcomes with client SMEs before implementation, then track results against agreed baselines.
  • Present findings clearly, separating measured results from hypotheses, and recommend what to improve or test next.
  • Build client confidence through accurate communication, practical training, and clear handoffs.

• Build reusable finance delivery practices

  • Convert insights from client work into reusable process maps, requirements patterns, evaluation methods, and implementation guidance.
  • Provide controller expertise across finance-focused discovery, solution design, and pursuit planning.
  • Surface delivery or client risks early and help set a high standard for finance AI work.

Requirements

• CPA background and substantial controller or controllership experience.
• Deep experience with ERP, accounting systems, or finance systems implementations and process change.
• Strong working knowledge of core accounting and finance processes and underlying systems, including order-to-cash, month-end close, or procure-to-pay.
• Evidence of implementing AI-supported workflows in finance or accounting, including a practical understanding of AI capabilities and limitations.
• Ability to translate accounting policy, controls, exceptions, and system behavior into requirements engineering teams can implement and test.
• Client-facing communication skills, including the judgment to ask precise questions, challenge assumptions, and explain tradeoffs clearly.
• Ownership across discovery, delivery, evaluation, and client review.

Preferred Qualifications

• Controller experience in a PE-backed company.
• Experience with AI-native ERP or finance automation platforms, accounting agents, or ERP integrations.
• Finance transformation consulting or experience across multiple priority finance processes.
• Experience defining evaluation methods and controls for AI systems used in finance.

Compensation & Benefits

• Competitive compensation (base salary + performance incentives tied to client outcomes).
• Equity options in a growing AI services company.
• Exposure to a wide range of industries and high-impact AI problems.
• Travel opportunities for on-site client engagements (if desired).

Location

• Client-based travel for on-site engagements (if desired).