[Hiring] Principal Enterprise Data Engineer @FICO

Role Description

Come join our engineering team in a hands-on technical role at the heart of a new discipline: Harness Engineering. As AI coding agents take on more of the software lifecycle, the hard part is no longer writing code - agents generate it faster than humans can review it, so the bottleneck shifts to verification and trust. Harness Engineering exists to break that bottleneck: engineering the environment that steers agents toward correct, maintainable, well-architected output so that quality is enforced by the system, not re-audited by a person on every change. We call that environment the harness (Agent = Model + Harness). As a Senior Harness Engineer you'll independently own whole harness subsystems, set the standards other engineers build to, and be involved in the end-to-end lifecycle of turning raw model capability into production-grade engineering.

What You’ll Contribute

• Design, build, deploy, and support core components of the harness - the guides, feedback loops, guardrails, and shared context that turn raw model capability into production-grade engineering.
• Own and evolve feedforward guides - agent instruction files, reusable skills, architectural rules, reference docs, and codemods - and drive team-wide standardisation.
• Build feedback sensors - custom linters, static analysis, structural and architecture-fitness tests, verification loops, and LLM-as-judge reviewers.
• Own quality gating and release criteria for agent-produced work, defining authority boundaries for what agents may merge unaided.
• Establish LLM testing infrastructure and evaluation approaches that ensure AI-generated output meets quality and safety thresholds.
• Run the steering loop - engineer controls to prevent repeated mistakes by agents.
• Decide where each control runs in the path to production - fast checks pre-commit, more expensive checks post-integration.
• Improve observability into agent work and track relevant measures - cost per merged PR, time-to-merge, review velocity, defect escape rate, and agent-PR survival rate.
• Partner with product and platform teams to turn specifications and acceptance criteria into enforceable controls.
• Serve as a source of technical expertise and mentor engineers across teams in harness practices.

Qualifications

• Bachelor's/Master's in Computer Science or related disciplines, or relevant commercial experience in software architecture, design, development, and testing.
• Seasoned software engineer with experience in large, complex codebases and a strong foundation in architecture and design.
• Hands-on experience with AI coding agents (e.g. Claude Code, Codex, or similar).
• Proven ability to build engineering tooling across a modern stack - linters, static analysis, CI/CD pipelines, containerised build/test environments.
• Experience with spec-driven development, context engineering, agent orchestration, fitness functions, and developer-platform work.
• A systems mindset - ability to encode "what good looks like" into mechanical, repeatable rules.
• Judgement about when to reach for deterministic versus inferential controls.
• Experience owning quality-gating processes and defining release criteria.
• Working knowledge of the security surface unique to autonomous agents.
• Experience with consumer/contract testing approaches (e.g. Pact).
• Excellent communication skills; able to articulate design with architects and drive standards across teams.

Benefits

• An inclusive culture strongly reflecting our core values: Act Like an Owner, Delight Our Customers and Earn the Respect of Others.
• The opportunity to make an impact and develop professionally by leveraging your unique strengths.
• Highly competitive compensation, benefits and rewards programs.
• An engaging, people-first work environment offering work/life balance, employee resource groups, and social events.