Finance Prompt Engineer

Equiforte; New York (NY); On-site; USD 150,000 - 230,000; Full time

Job summary

Equiforte is seeking a Finance Prompt Engineer to be embedded with client firms across New York, Boston, and Chicago. You will understand CFO reporting language, design prompts and validation logic, and ensure AI outputs meet required financial standards.

You will sit with clients during quarterly closes, advise on LP reporting workflows, and feed field learnings back to product to strengthen the platform’s reliability.

Qualifications

• 5+ years in private capital finance (fund accounting, LP reporting, or close).
• Familiar with LP reporting mechanics and ILPA standards.
• Experience using AI tools in financial contexts.
• Comfort configuring software logic and debugging outputs.

Responsibilities

• Embed with client firms during quarterly closes and reporting cycles.
• Translate client workflows into prompts, validation rules and configurations.
• Act as Equiforte's on-the-ground expert for AI-native workflows.
• Provide feedback to product teams to improve platform reliability.

Skills

Private capital finance

LP reporting

ILPA standards

Client facing

AI in finance

System configuration

Financial methodology

Job description

Client Delivery | New York / Boston / Chicago | Full-time

About The Role

Finance Prompt Engineers are the most distinctive role at Equiforte — and among the most critical. You are not a traditional engineer, and you are not a traditional finance professional. You are both: a domain expert with deep, hands-on experience in private capital finance who has developed the instinct for how AI systems can and cannot be trusted with financial data.

In this role, you sit with clients. You understand their reporting workflows in the same language their CFOs use. You know what a correctly structured waterfall looks like, what a compliant LP letter requires, and where AI outputs need human judgment before they ship. You translate that understanding into the prompts, validation logic, and workflow configurations that make Equiforte's platform produce results that clients trust absolutely.

This is a forward-deployed role. You will be embedded with client firms — attending their quarterly close cycles, sitting in on their reporting reviews, and acting as Equiforte's on-the-ground expert as they adopt AI-native workflows. Your feedback loops directly into product: every pattern you encounter in the field becomes an improvement to the platform.

This role reports to

the Head of Client Delivery and works closely with the Engineering, ML Engineering, and Product teams.

You Might Be a Fit If

• You have 5+ years of hands-on private capital finance experience — fund accounting, LP reporting, capital call processing, valuation, or quarterly close at a private equity, private credit, or infrastructure firm, or at a fund administrator serving those firms
• You are deeply familiar with the mechanics of institutional LP reporting: ILPA standards, side letter provisions, carried interest calculations, and the documentation requirements of sophisticated LPs
• You have started using AI tools seriously and have developed strong intuitions about where they produce reliable outputs and where they fail — especially in financial contexts
• You are technically curious: you do not need to write code, but you are comfortable working inside software systems, configuring logic, and debugging output quality issues
• You are effective in client-facing environments at the CFO and Controller level — you communicate with precision and can hold your own in conversations about financial methodology
• You thrive in ambiguous, early-stage environments where the playbook is being written in real time
• You want to be a builder, not just an implementer — you see your field experience as a resource for making the product better

Application Questions

Please be prepared to address the following when you apply:

• Describe a quarterly close or LP reporting process you have owned or been deeply involved in. What were the most error-prone steps, and how were they managed?
• Have you used AI tools — including LLMs or document extraction tools — in a financial context? What worked, what did not, and how did you validate the outputs?
• What aspect of private capital finance do you think is hardest to automate reliably, and why?