Principal AI Deployment Manager, Controller

We are a technology services company dedicated to helping organizations build and deploy cutting-edge AI solutions. From generative AI and custom LLM integrations to predictive analytics and intelligent automation, we work across industries to bring real-world AI applications to life. Our projects combine deep technical expertise with hands-on client collaboration to solve high-impact problems.

The Principal AI Deployment Manager, Controller brings controller-level accounting depth to AI implementation work across finance teams. You will work with finance leaders and subject-matter experts to understand how their processes and systems work today, identify where AI can improve them, and shape a practical path from discovery to an evaluated workflow.

The work centers on order-to-cash, month-end accounting close, and procure-to-pay. You will translate accounting policies, controls, system behavior, data needs, and exceptions into clear requirements, then partner with Forward Deployed Engineers to design and deliver a finance workflow that can be tested and reviewed. This is a client-facing role for a controller who has implemented AI workflows and can connect finance judgment to technical delivery.

Eliza works AI-first and expects everyone in the company to use AI tools thoughtfully in their work. We move quickly, work through ambiguity, and stay accountable for outcomes. You will receive ownership of client problems, not a fixed script.

• 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 clarify what a successful outcome would mean to the finance team.

• Assess process fit, data readiness, risk, and where human review or approval must remain.

• Turn 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.

• Make clear recommendations about where AI is useful, where standard automation is sufficient, and where a process is not ready.

• 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.

• Define evaluation criteria and business outcomes with client SMEs before implementation and track results against agreed baselines.

• Present findings clearly, separate measured results from hypotheses, and recommend what to improve or test next.

• Build client confidence through accurate communication, practical training, and clear handoffs.

• Turn lessons from client work into reusable process maps, requirements patterns, evaluation methods, and implementation guidance.

• Contribute controller expertise to finance-focused discovery, solution design, and pursuit planning.

• Raise delivery or client risks early and help set a high standard for finance AI work.

• 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 (e.g., order-to-cash, month-end close, or procure-to-pay)

• Evidence of implementing AI-supported workflows in finance or accounting, with a practical understanding of AI capabilities and limitations.

• Ability to translate accounting policy, controls, exceptions, and system behavior into requirements that engineering teams can implement and test.

• Client-facing communication skills and the judgment to ask precise questions, challenge assumptions, and explain tradeoffs clearly.

• Ownership of outcomes through discovery, delivery, evaluation, and client review.

• 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 work across multiple priority finance processes.

• Experience defining evaluation methods and controls for AI systems used in finance.