8+ years in software engineering, including 3+ years in a leadership role.
Experience leading a large legacy modernization project to production.
Proven track record in designing and running AI-assisted engineering workflows.
Strong proficiency in C#/.NET and familiarity with older .NET and SQL Server code.
Practices test-driven development and understands quality beyond coverage metrics.
Knowledge of CI/CD, trunk-based development, and cloud deployment (preferably Azure).
Responsibilities
Own the roadmap for rebuilding legacy capabilities based on business risk and value.
Design and manage AI-assisted engineering workflows for code generation and review.
Create and maintain prompts and instructions for AI agents to align with architecture standards.
Enforce a test-first approach, ensuring AI-generated code meets the same quality gates as human-written code.
Review AI-generated code with a focus on financial accuracy and data isolation.
Document business rules from legacy code to preserve critical information.
Collaborate with Compliance and Product teams to ensure regulatory requirements are met in tests.
Monitor and report on delivery metrics and AI effectiveness, including rework rates.
Benefits
Full-time employment with a focus on work-life balance.
Opportunity to lead innovative projects in AI and software engineering.
Collaborative work environment with a focus on team development.
Access to cutting-edge technology and tools in a rapidly evolving field.
Full Job Description
AI Refactoring Manager - Hive Financial Systems
Location: Atlanta, Georgia (Buckhead)
Employment Type: Full-time, Exempt
Department: Engineering
Role Overview
We 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.
