Lead AI Engineer - Audit Automation

CyberCoders, Inc.; United States; On-site; USD 180,000 - 230,000; Full time

Job summary

CyberCoders, Inc. is seeking the first engineer to rebuild a high-stakes audit workflow for a specialized CPA firm. You will own a greenfield AI platform end-to-end, working directly with practitioners to replace thousands of hours of manual rekeying.

You will embed with partners to map work, design intelligent agents, implement a governed data layer, and deliver production-ready Python/TypeScript services that scale across clients.

Qualifications

• Experience owning a live Python backend service (typed, async)
• Shipped LLM-backed workflow systems with structured outputs
• Production deployment of agents and tool-calling with measurable accuracy
• Strong data boundary judgment and awareness of zero-data-retention requirements
• Relational data modeling with PostgreSQL and migrations (SQLAlchemy/Alembic)

Responsibilities

• Map and automate the audit lifecycle with AI agents embedded with partners and managers
• Build a governed data layer with source, author, verification status, and timestamp
• Develop automated roll-forward to generate current-year workpapers from prior-year files
• Create intelligent document intake that extracts structured records and drafts follow-up questions
• Render results into standard templates by writing automated output back into Excel/Word workpapers and secure final archive
• Own production operations including access control, policy enforcement, cost monitoring, incident response, and parallel-run discipline
• Set reusable patterns so a client solution becomes a baseline for others

Skills

Python

TypeScript

LLM workflows

PostgreSQL

Tools

Pydantic

SQLAlchemy

Alembic

Azure OpenAI

OpenTelemetry

Job description

We are a specialized CPA firm that runs high-stakes employee benefit plan audits and we are hiring the first engineer to rebuild our entire audit workflow from the inside out. If you want to own a greenfield AI platform end-to-end, work directly with the practitioners who use it every day, and see your code replace thousands of hours of manual rekeying, this is that role.

What you'll do

• Map and automate the audit lifecycle: embed with audit partners and senior managers to understand exactly how work flows, then design and ship intelligent agents that eliminate manual steps one by one.
• Build a governed data layer: every value written by an AI agent carries its source, author, verification status, and timestamp so a partner can inspect and sign off with confidence.
• Develop automated roll-forward: generate current-year workpapers from prior-year files automatically, flagging year-over-year changes for human review instead of requiring manual retyping.
• Create intelligent document intake: extract structured records from client-supplied and third-party files; auto-draft follow-up questions when data is missing or contradicts historical records.
• Render results into standard templates: write automated output back into the firm's Excel and Word workpapers with full visual and structural fidelity; lock records securely on final archive.
• Own production operations: access control, model policy enforcement, cost monitoring, incident response, and parallel-run discipline before any automated step displaces a manual one.
• Set reusable patterns: a solution built for one client becomes the baseline configuration for all others, compounding your impact across the practice.

What you need

• Production Python service ownership: you have been the accountable engineer for a live, typed, async backend service and can walk through real failures and how you resolved them. Strong TypeScript engineers with a compelling stack-alignment case will also be considered.
• LLM-backed workflow engineering: you have shipped systems where LLM outputs are captured as strictly structured data (Pydantic, Instructor, or native structured-output APIs), validated before ingestion, and wrapped in deterministic logic.
• Agent and tool-calling experience: practical production deployment of agents, tool-calling, or MCP integrations with measurable accuracy tracking.
• Data boundary judgment: you have made deliberate, defensible decisions about how sensitive data is isolated before it reaches external APIs; you understand zero-data-retention agreements and regional endpoint configurations.
• Relational data modeling: strong PostgreSQL experience and comfort with migration frameworks (SQLAlchemy/Alembic); you think of the platform as an authoritative data model first, application layer second.

Nice to have

• Programmatic Office automation: generating formatted Excel and Word files from code (openpyxl, python-docx, Office.js) including formulas, named ranges, and sheet protection.
• Cloud infrastructure on Azure: AKS, App Service, Blob Storage, Azure OpenAI endpoint management.
• Enterprise agent tooling: Azure AI Foundry, Copilot Studio, Power Platform, or Dataverse.
• Async pipeline orchestration via task queues (taskiq, RabbitMQ, Redis) or state machines.
• Continuous LLM evaluation: custom regression suites that track output quality as models or prompts change.
• Cross-functional rollout leadership with non-technical subject matter experts resulting in sustained adoption.
• Domain knowledge in audit, accounting, compliance, or payroll.
• Document extraction experience (Azure Document Intelligence, OCR/document-understanding platforms).
• Modern observability (OpenTelemetry to cloud monitoring suites).