Eliza
Artificial Intelligence & Machine Learning Services
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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.
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)
Direct client ownership of finance AI work from discovery through implementation planning and outcome review.
Close partnership with engineers and finance leaders on real operational problems.
The opportunity to shape reusable methods for finance AI delivery.
Variety across finance processes, systems, and client environments.
A high-ownership role with room to grow as the OCFO practice develops.
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