F

FICO

Predictive credit risk scoring & analytics

Senior Harness Engineer

Test Automation EngineeringConfirmed live in the last 24 hours
Full-timeRemote in BulgariaNo salary listedRemote

About the Job

Requirements

  • A Bachelor's or Master's degree in Computer Science or a related discipline, or relevant commercial experience in software architecture, design, development, and testing.
  • Seasoned software engineering experience with large, complex codebases and a strong foundation in architecture and design, with 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 and test environments, and instrumentation and observability; familiarity with agent instruction conventions such as AGENTS.md.
  • Experience with specification-driven development, context engineering, agent orchestration, fitness functions, and developer-platform work.
  • Ability to encode engineering quality standards into mechanical, repeatable rules and a systems mindset focused on improving the environment rather than individual outputs.
  • Judgment about using deterministic computational controls such as type checkers, linters, and structural or architecture-fitness tests versus inferential large language model-based controls such as AI code review and LLM-as-judge, including understanding 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 specific to autonomous agents, including prompt injection, tool and permission scoping, sandboxed execution, and audit trails for agent actions, and designing least-privilege guardrails.
  • Experience with consumer and contract testing approaches such as Pact to validate service integrations across distributed systems.
  • Ability to articulate designs to architects and drive standards across teams.

Responsibilities

  • Design, build, deploy, and support core harness components, including guides, feedback loops, guardrails, and shared context that steer AI agents toward production-grade engineering; focus on systems and leverage rather than hand-writing application code.
  • Own and evolve feedforward guides, including agent instruction files, reusable skills, architectural rules, reference documentation, and codemods, and drive team-wide standardization.
  • Build feedback sensors, including custom linters, static analysis, structural and architecture-fitness tests, verification loops, and LLM-as-judge reviewers, to catch issues before human review.
  • Own quality gating and release criteria for agent-produced work, define what agents may merge without assistance, and establish escalation rules for work that must be routed to a human.
  • Establish LLM testing infrastructure and evaluation approaches to ensure AI-generated output meets quality and safety thresholds; apply consumer and contract testing where service integration reliability matters.
  • Run the steering loop by engineering controls to prevent repeated agent mistakes, treat repository knowledge such as documentation, specifications, and context as the system of record, and reduce drift through continuous cleanup.
  • Determine where controls run in the production path, including fast pre-commit checks, more expensive post-integration checks, and continuous sensors for drift outside the change lifecycle; keep quality controls as early in the process as economical.
  • Improve observability into agent work and track cost per merged pull request, time to merge agent-assisted pull requests, review velocity relative to pull request size, defect escape rate, and agent-pull-request survival rate; use these measures to determine future 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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