F

FICO

Predictive credit risk scoring & analytics

Lead Harness Engineer

Software EngineeringConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • A Bachelor's or Master's degree in Computer Science or a related discipline, or relevant experience in software architecture, design, development, and testing.
  • A strong software engineering background working in large codebases, with attention to architecture, testing, and maintainability.
  • Experience building tooling across a modern stack, including linters and static analysis, continuous integration pipelines, containerized build and test environments, and instrumentation and observability.
  • Hands-on experience with artificial intelligence coding agents such as Claude Code or Codex.
  • Experience with specification-driven development, context engineering, agent orchestration, fitness functions, and developer-platform work.
  • Ability to encode quality standards into mechanical, repeatable rules and judge when to use deterministic computational controls versus inferential large-language-model controls.
  • Understanding of the cost, speed, and reliability trade-offs between deterministic and large-language-model-based controls.
  • Ability to clearly articulate designs with architects and discuss strategy and requirements with teams.

Responsibilities

  • Design, develop, deploy, and support harness components including guides, feedback loops, guardrails, and shared context.
  • Build feedforward guides such as agent instruction files, reusable skills, architectural rules, reference documentation, and codemods.
  • Build feedback sensors including custom linters, static analysis, structural and architecture-fitness tests, verification loops, and large-language-model-as-judge reviewers.
  • Engineer controls that prevent agents from repeating mistakes and maintain repository knowledge, documentation, specifications, and context in a form legible to agents.
  • Contribute to quality gates, release criteria, and large-language-model testing to ensure AI-generated output meets quality and safety thresholds.
  • Improve observability into agent work and track metrics including 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.
  • Evaluate the stability, compatibility, scalability, interoperability, and performance of harness components.
  • Learn new techniques in agent-augmented engineering and serve as a source of technical expertise and mentor junior team members.

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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