Senior AI Deployment Manager, Finance

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 Senior AI Deployment Manager, Finance brings strong finance-domain experience to client work that applies AI to accounting and finance workflows. You will lead client discovery and delivery workstreams, turn unclear process needs into a practical implementation plan, and partner with Forward Deployed Engineers to move the work forward.

The priority areas are order-to-cash, month-end accounting close, and procure-to-pay. You should understand how finance teams operate and have implemented AI workflows in real work. Deep accounting process judgment matters more than AI specialization; you should be able to learn new tools quickly, explain their limits, and bring engineering in where technical decisions require it.

Eliza works AI-first and expects everyone in the company to use AI tools thoughtfully in their work. Client priorities move quickly, and the work can be ambiguous. You will receive ownership of a workstream, not a fixed script, and will be expected to communicate risks and tradeoffs early.

• Run client sessions to understand the finance outcome, users, current process, systems, data, controls, exceptions, and pain points.

• Focus discovery on O2C, month-end close, and P2P; establish the facts needed to assess value and feasibility.

• Identify which process steps are suitable for AI, where conventional automation is a better fit, and where human judgment must remain.

• Translate finance needs into product requirements, prioritized MVP scope, acceptance criteria, and a work plan an FDE can build against.

• Make dependencies, access needs, data quality, risks, and assumptions explicit.

• Recommend a sequence that balances business value, feasibility, timing, and quality.

• Manage scope, milestones, client decisions, risks, handoffs, and status through testing and client review.

• Partner closely with an FDE, keeping business requirements and technical decisions aligned without taking over engineering ownership.

• Evaluate workflow performance using agreed test cases and success criteria; surface failure modes and propose next steps.

• Facilitate working sessions with finance SMEs and communicate progress and tradeoffs in clear language.

• Present concise updates that distinguish verified results from estimates or hypotheses.

• Help client teams understand the workflow, its limitations, review requirements, and operational handoffs.

• Support finance-focused pursuits and scope discussions with grounded process and implementation judgment.

• Coach Associates or teammates on client delivery and finance-workstream execution where assigned.

• Package reusable discovery questions, process maps, requirements patterns, and delivery lessons.

• Strong finance-domain experience in finance transformation, program finance, corporate finance, or a related field.

• Practical knowledge of at least one of order-to-cash, month-end close, or procure-to-pay. Experience in AP/AR, invoicing, ERP, or finance systems is relevant.

• Evidence of implementing AI-supported workflows in real work; casual AI tool use alone is not enough.

• Ability to lead client-facing discovery and delivery, structure ambiguous problems, and own work through measurable review.

• Can translate finance process requirements into clear implementation considerations and partner effectively with engineers.

• Working understanding of AI capabilities and limitations, with sound judgment about human review and operational risk.

• Strong writing, presentation, stakeholder management, and prioritization skills.

• Finance transformation consulting or professional-services delivery experience.

• FP&A, program finance, or analytical experience.

• Controller, CPA, or accounting-operations background.

• ERP implementation, finance automation, or finance-focused AI product experience.

• Experience in a PE-backed company.