AI Refactoring Manager - Hive Financial SystemsLocation: Atlanta, Georgia (Buckhead)
Employment Type: Full-time, Exempt
Department: Engineering
Role OverviewWe are looking for an
AI Refactoring Manager to lead the rebuild of our legacy lending platform onto a new multi-portfolio architecture, with AI coding agents doing much of the code generation and people owning the judgment, review, and quality bar. This is a hands-on leadership role: you will set the method, run the work, and still read the code.
Our legacy estate is roughly 50 repositories built one portfolio at a time. We are replacing it with true microservices on .NET, Azure, and Kubernetes, designed to scale to 30 lending portfolios through configuration rather than code forks. This is a rebuild, not a line-by-line port: the legacy code tells us what the business needs, and its architecture is not the target. In regulated lending, a wrong amortization schedule, missed disclosure, or leaked borrower record is a compliance event, not just a bug-so speed only counts if quality holds.
Key Responsibilities- Own the rebuild roadmap: sequence which legacy capabilities move first based on business risk, dependencies, and value.
- Design and run the AI-assisted engineering workflow: how agents read legacy code, extract business rules, draft services, and hand off to human review.
- Build and maintain the prompts, agent instructions, and reusable skills that encode Hive's architecture standards so AI output starts close to right.
- Enforce a test-first discipline: failing acceptance tests exist before code is written, and AI-generated code passes the same gates as human code.
- Personally review AI-generated code and set the review bar for the team, with particular attention to money math, borrower data, and portfolio isolation.
- Separate live code from dead code in the legacy estate and document the business rules buried in it before they are lost.
- Partner with Compliance and Product so regulated behavior (disclosures, payments, adverse action, consent) is captured and proven in tests.
- Track and report delivery health (lead time, deployment frequency, change failure rate, recovery time) and AI effectiveness (rework rate, defects traced to AI output).
- Coach engineers on working well with AI agents, and decide where AI should not be used.
- Evaluate AI coding tools and models, and manage their cost, access, and security posture.
What Success Looks Like in the First 12 Months- By Month 3: A documented, repeatable AI-assisted rebuild method is in daily use by every rebuild team.
- Per service: Legacy business rules and acceptance criteria are extracted and signed off by Product before rebuild begins.
- Every release: Rebuilt services meet 90%+ line and branch test coverage, with failing tests written before code.
- By Months 6 and 12: Lead time and deployment frequency improve quarter over quarter without a rise in change failure rate.
- By Month 12: An agreed set of legacy capabilities is live on the new platform with legacy equivalents switched off, and zero compliance or data-isolation defects from AI-generated code have reached production.
Qualifications- 8+ years in software engineering, including 3+ years leading engineers or a technical program.
- Has led at least one large legacy modernization (monolith to microservices, re-platform, or rewrite) through to production.
- Proven experience in agentic AI engineering and workflow creation: designing, building, and running multi-step AI agent workflows (e.g., with Claude Code or similar agent frameworks) on production work, with a clear view of where they fail.
- Strong in C#/.NET; comfortable reading older .NET and SQL Server code.
- Practices test-driven development and can explain why coverage numbers alone are not quality.
- Working knowledge of CI/CD, trunk-based development, and cloud deployment (Azure preferred).
Preferred Skills- Experience in lending, payments, or another regulated financial domain (TILA/Reg Z, NACHA, FCRA, GLBA).
- Kubernetes, Helm, Terraform or Bicep, and GitOps tooling such as Argo CD.
- Has written agent instructions, prompt libraries, or internal AI tooling used by a team.
- Multi-tenant system design where data isolation between tenants is a hard requirement.
- Familiarity with DORA metrics and observability tools such as DataDog.
Competencies We Hire For- Agentic Engineering Judgment: Knows when to trust, verify, or reject agent output, and can show agents they built and the failures they caught.
- Workflow Design: Breaks complex engineering work into agent workflows with clear steps, checkpoints, and human review points, and makes them repeatable for the team.
- Quality Ownership: Holds the bar under deadline pressure; ships nothing they would not defend in an audit.
- Systems Thinking: Sees how a change in one service ripples across others and across portfolios.
- Clear Communication: Explains technical tradeoffs to Compliance, Product, and executives in plain terms.
- Coaching: Raises the team's skill in building and using agent workflows rather than becoming the bottleneck.
