F

FICO

Predictive credit risk scoring & analytics

Principal Harness Engineer

Test Automation EngineeringPosted on Nov 3
Full-timeRemote in BulgariaNo salary listedRemote

About the Job

Requirements

  • Bachelor's or Master's degree in Computer Science or related disciplines, or relevant commercial experience in software architecture, design, development, and testing.
  • Experience as a seasoned software engineer working with large, complex codebases, with a strong foundation in architecture and design and a focus on testing and maintainability.
  • Hands-on experience with AI coding agents such as Claude Code, Codex, or similar, and an understanding of where they succeed and fail.
  • Ability to build engineering tooling across a modern stack, including linters and static analysis, continuous integration and continuous delivery pipelines, containerized build/test environments, and instrumentation/observability; familiarity with agent instruction conventions such as AGENTS.md.
  • Experience with spec-driven development, context engineering, agent orchestration, fitness functions, and developer-platform work.
  • Ability to encode engineering standards into mechanical, repeatable rules and apply a systems mindset.
  • Judgment about when to use deterministic computational controls versus inferential, large language model-based controls, with an understanding of their cost, speed, and reliability trade-offs.
  • Experience owning quality-gating processes and defining release criteria to ensure engineering standards are consistently met.
  • Working knowledge of security risks unique to autonomous agents, including prompt injection, tool/permission scoping, sandboxed execution, and audit trails for agent actions, and how to design least-privilege guardrails.
  • Experience with consumer/contract testing approaches such as Pact to validate service integrations across distributed systems.
  • Ability to articulate designs with architects and drive standards across teams.

Responsibilities

  • Design, build, deploy, and support core harness components, including guides, feedback loops, guardrails, and shared context that turn model capability into production-grade engineering.
  • Own and evolve feedforward guides, including agent instruction files, reusable skills, architectural rules, reference documents, and codemods, and drive team-wide standardization.
  • Build feedback sensors, including custom linters, static analysis, structural and architecture-fitness tests, verification loops, and large language model-as-judge reviewers, to identify issues before human review.
  • Own quality gating and release criteria for agent-produced work, defining what agents may merge unaided and escalation rules for work requiring human review.
  • Establish large language model testing infrastructure and evaluation approaches to ensure AI-generated output meets quality and safety thresholds; apply consumer/contract testing where service integration reliability matters.
  • Run the steering loop by engineering controls to prevent repeated agent mistakes, and maintain repository knowledge as the system of record through continuous cleanup to prevent drift.
  • Determine where controls run in the path to production, including fast pre-commit checks, more expensive post-integration checks, and continuous sensors that detect drift outside the change lifecycle.
  • Improve observability into agent work and track cost per merged pull request, time-to-merge for agent-assisted pull requests, review velocity relative to pull request size, defect escape rate, and agent-pull-request survival rate to guide investment.
  • Partner with product and platform teams to turn specifications and acceptance criteria into enforceable controls.
  • Provide technical expertise and mentor engineers across teams in harness practices and the effective, responsible use of AI tools.

About the Company

FICO

Predictive credit risk scoring & analytics

FICO provides data analytics and decision-management tools for credit risk and lending, with the FICO score as its flagship measure of consumer credit risk used by lenders. The score is calculated from a user’s credit report data to predict loan repayment likelihood, and FICO software applies analytics and decision rules to guide loan approvals, pricing, fraud detection, and account management. Unlike many competitors, FICO combines a widely adopted score with an end-to-end suite of predictive analytics and decision-management tools that cover the full lending lifecycle. Its goal is to help financial institutions make better lending and risk decisions, prevent fraud, and optimize customer relationships through data-driven, rules-based systems.

Company size
1,001-5,000
Company stage
IPO
Headquarters
San Jose, CA
Founded
1956

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