Note: The job is a remote job and is open to candidates in USA. FICO is a leading analytics and decision management company that uses data-driven insights, advanced analytics, machine learning, and sophisticated algorithms to support smarter decisions across industries. The Principal Engineer will design, build, and operate harness systems that guide AI coding agents toward reliable, maintainable, and production-grade software. The role focuses on engineering feedback loops, guardrails, quality gates, testing infrastructure, observability, and standards for responsible agent-assisted development.
Responsibilities
- 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. This is a hands-on role focused on systems and leverage, not hand-writing application code
- Own and evolve feedforward guides — agent instruction files, reusable skills, architectural rules, reference docs, and codemods — and drive team-wide standardization so agents get it right the first time
- Build feedback sensors — custom linters, static analysis, structural and architecture-fitness tests, verification loops, and LLM-as-judge reviewers — that catch issues automatically before they reach human reviewers
- Own quality gating and release criteria for agent-produced work, defining authority boundaries for what agents may merge unaided and the escalation rules for what must route to a human
- Establish LLM testing infrastructure and evaluation approaches that ensure AI-generated output meets quality and safety thresholds; apply consumer/contract testing (e.g. Pact) where service integration reliability matters
- Run the steering loop — when an agent repeats a mistake, engineer a control so it can't happen again — and treat repository knowledge (docs, specs, context) as the system of record, fighting drift with continuous garbage collection
- Decide where each control runs in the path to production — fast checks pre-commit, more expensive checks post-integration, and continuous sensors that scan for drift outside the change lifecycle — keeping quality as far left as is economical
- Improve observability into agent work and track the measures that matter — cost per merged PR, time-to-merge for agent-assisted PRs, review velocity relative to PR size, defect escape rate, and agent-PR survival rate — using them to decide where to invest next
- 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 and the effective, responsible use of AI tools
Skills
- Seasoned software engineer with experience in large, complex codebases and a strong foundation in architecture and design; you care deeply about testing and maintainability
- Hands-on experience with AI coding agents (e.g. Claude Code, Codex, or similar) and a well-developed feel for where they succeed and fail
- Proven ability to build engineering tooling across a modern stack — linters and static analysis, CI/CD pipelines, containerized build/test environments, and instrumentation/observability — plus familiarity with agent instruction conventions such as AGENTS.md
- Experience with spec-driven development, context engineering, agent orchestration, fitness functions, and developer-platform work
- A systems mindset — you'd rather fix the environment than fix one output — and the ability to encode "what good looks like" into mechanical, repeatable rules
- Judgement about when to reach for deterministic, computational controls (type checkers, linters, structural/architecture-fitness tests) versus inferential, LLM-based ones (AI code review, LLM-as-judge) — and an understanding of the cost, speed, and reliability trade-offs between them
- Experience owning quality-gating processes and defining release criteria to ensure engineering standards are consistently met
- Working knowledge of the security surface unique to autonomous agents — prompt injection, tool/permission scoping, sandboxed execution, and audit trails for agent actions — and how to design least-privilege guardrails around them
- Experience with consumer/contract testing approaches (e.g. Pact) to validate service integrations across distributed systems
- Excellent communication skills; able to articulate design with architects and drive standards across teams
- Bachelor's/Master's in Computer Science or related disciplines, or relevant experience in software architecture, design, development, and testing
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