AI Finance Engineer Job Description

An AI Finance Engineer is the person on a company's finance team who writes the code. The job sits inside the CFO's organization, not in a product group and not at a software vendor, and the customers are colleagues down the hall: staff accountants, the assistant controller, the revenue team, treasury and the FP&A analysts who build the forecast. The engineer's work is judged by one calendar, the close, and by one audience that never goes away, the auditors.

The close is the main product

The work starts with a map of the month-end close. The engineer sits with the controller's team, lists every task on the close checklist and asks which ones are mechanical: bank and credit card reconciliations, prepaid and accrual schedules, intercompany matching, fixed asset roll-forwards, flux explanations for variance review. Those become candidates for automation. A reconciliation tool might pull the general ledger from the ERP and transactions from the bank feed, propose matches, write a plain-English note on each exception and send open items to the accountant who owns the account. An accrual agent might read contracts and purchase orders, calculate the entry and attach the support, then stop and wait for a person to approve it before anything posts.

Forecasting and FP&A support

On the planning side, the engineer builds pipelines that keep the forecast current. That can mean pulling bookings, billings and headcount from source systems into the warehouse, running models that project revenue and expense, and loading the results into the planning tool the FP&A team already uses. It can also mean giving analysts a way to ask questions of the data in plain language and get an answer that cites the underlying query.

Controls are part of the build

Because these tools touch the books, every one has to be auditable. The engineer logs what went into each AI step and what came out, keeps a named reviewer on anything that affects a balance, versions prompts and models the way an accounting team versions policies, and writes the documentation internal audit and the external auditors will ask for. A change to a prompt is treated like a change to a spreadsheet formula in a key control: tested, approved and recorded.

Ownership over time

The defining trait of the job is that the engineer stays. A consultant or vendor team can ship a pilot and leave; this person is still there when the chart of accounts is restructured, a new subsidiary is acquired, the ERP is upgraded or a model's accuracy slips in a busy quarter. Much of the week goes to maintenance: watching accuracy and cost, fixing an integration that broke the night before close, retraining the team on a changed workflow and deciding which manual step to automate next.

The work calls for two vocabularies at once. In one meeting the engineer is talking cutoff, materiality and segregation of duties with the controller; in the next it is API rate limits, retrieval quality and evaluation scores with IT.

There is no single path into this job yet, and it can be entered from either side. A software or data engineer can join a finance systems team and learn how the ledger works; an accountant or FP&A analyst can start automating their own work in Python and SQL and keep going. Either way, the job asks for someone who can build production software and also explain why a reconciliation does not tie.

Education

As a rough guide, a bachelor's degree in computer science, information systems, accounting or finance is the usual starting point. Engineers without accounting coursework benefit from a financial accounting class or self-study that covers double-entry bookkeeping, accrual accounting, revenue recognition and the close cycle. Accountants moving the other way need solid programming, data modeling and software testing skills.

Experience

As a rough guide, candidates bring 3 to 6 years in software engineering, data engineering or finance systems work, with real exposure to a month-end close. Useful experience includes administering or integrating an ERP such as NetSuite, SAP S/4HANA or Oracle Fusion Cloud ERP; building data pipelines into a warehouse; working with close and reconciliation software; and supporting an audit, including walkthroughs and control testing.

Technical skills

The core technical kit is Python, SQL, REST APIs and a cloud data warehouse, plus working knowledge of large language model APIs, retrieval over company documents, agent orchestration and evaluation design. Just as important is software discipline that auditors can follow: version control, code review, test suites, access controls and change logs.

Finance skills and credentials

The finance knowledge that matters most is practical: account reconciliations, journal entries, accruals, intercompany eliminations, revenue recognition basics, the close calendar and how internal controls over financial reporting are designed and tested. The FP&A profession already weighs this mix; in the 2025 AFP FP&A Benchmarking Survey, a global association survey of FP&A practitioners, 83% of respondents rated technology and data skills on par with finance skills in desirability.

No credential is required. Accountants entering the role may hold the CPA license, issued by state boards of accountancy, or the Certified Management Accountant (CMA) from the Institute of Management Accountants. The CPA Exam itself changed with the launch of a new exam in January 2024, which added a choice of discipline section; one option, Information Systems and Controls, covers IT audit, SOC engagements, data management, security and privacy, according to AICPA & CIMA. On the engineering side, the AWS Certified Machine Learning Engineer - Associate from Amazon Web Services shows cloud machine learning skills.

AI Finance Engineer is a new title, and no federal survey counts it. The nearest BLS occupation for this kind of build work is software developers, which employed 1,687,890 people in May 2025, but BLS does not report how many of them work inside corporate finance departments or on accounting tools. Because the title is not standardized, anyone tracking openings can search on the duties, such as close automation and reconciliation tooling, as well as on the title itself.

Where finance teams stand on AI

One data point on adoption comes from the finance profession itself. In the 2025 AFP FP&A Benchmarking Survey, an association survey by the Association for Financial Professionals with 362 responses from FP&A practitioners around the world, just 23% of respondents used AI on a daily, weekly or monthly basis, and 40% were in the testing phase with plans to implement AI in the next year. The space between testing and daily use is where this role works: somebody on the finance team has to turn a pilot into a tool that runs every month and survives an audit.

New controls guidance

The governance side moved in 2026. In February, the Committee of Sponsoring Organizations of the Treadway Commission (COSO) released Achieving Effective Internal Control Over Generative AI, which translates its Internal Control-Integrated Framework into audit-ready guidance for governing generative AI, including control mapping and control considerations across the data-to-decision lifecycle. COSO's own announcement notes that organizations are already using AI-enabled tools to automate reconciliations. For an engineer building close tools at a company that reports on internal controls, the publication gives a shared vocabulary with internal audit, the audit committee and the external auditor.

What the work looks like over a career

The job has two natural directions. One leads deeper into finance: owning finance systems and data for the whole CFO organization, or moving toward a controller or chief accounting officer track with unusual technical depth. The other leads deeper into engineering: leading a team that builds AI tools for several back-office functions, or moving to a platform or architecture role. Experience with audited systems can carry over to other regulated settings, because the habits of logging, review and change control apply wherever financial data is involved.

Risks to weigh

Because the title is new, job security depends on the tools paying off. A project that never leaves the pilot stage, or that auditors cannot test, is hard to defend at budget time. A useful record to build is a set of tools that run every close, with documented controls and measured time saved. Pay benchmarks are also thin, since BLS reports no separate wage data for the title.

Dear Hiring Manager,

I am applying for the AI Finance Engineer role on your finance team. For the past four years I have worked on the finance systems team at a mid-size software company, and for the last two I have been the engineer the controller calls when a close task can be automated.

My first project was our bank reconciliation. Our staff accountants were matching thousands of transactions a month across six accounts by hand. I built a pipeline that pulls the general ledger from NetSuite and the bank feeds into our warehouse, proposes matches, writes a short explanation for each exception and assigns open items to the account owner. The accountants still review and sign off on every reconciliation, but the work moved from matching to investigating, and the close checklist for cash shrank by two days.

From there I built an accrual assistant that reads vendor contracts and open purchase orders, calculates monthly accruals and attaches the support for review. Before it went live I sat down with internal audit and our external auditors to walk through the design. We agreed on logging for every input and output, a named reviewer for each entry, and a change process for prompts and models that mirrors how we handle key spreadsheet controls. The tool passed its first year-end audit without a finding.

I also maintain what I build. When we restructured our chart of accounts last spring, I updated the matching rules, reran our evaluation set of past closes and retrained the team before the next month-end.

I would bring the same approach to your team: start with the controller's checklist, build tools the auditors can test, and stay accountable for them after launch. I would welcome the chance to talk.

Sincerely, Jordan Avery