FICO
Website:
fico.com
Job details:
**Location: This role would require you to work from Bangalore, India office (3days/ week)
**Role type: Individual contributor
The Opportunity
As Lead Harness Engineer, you'll build and own the harness's individual controls, end-to-end from problem to production. As AI agents write more code, the bottleneck shifts from writing to verification — agents outpace human review. Harness Engineering fixes this by building the environment (the harness) that steers agents toward correct, maintainable output, so quality is enforced by the system, not checked by hand
What We're Seeking
- Bachelor's/Master's in Computer Science or related disciplines, or relevant experience in software architecture, design, development, and testing.
- Strong software engineering background; you've worked in large codebases and care about architecture, testing, and maintainability.
- Comfortable building tooling across a modern stack — linters and static analysis, CI pipelines, containerized build/test environments, and instrumentation/observability — and familiar with agent instruction conventions such as AGENTS.md.
- Hands-on experience with AI coding agents (e.g. Claude Code, Codex, or similar) and a feel for where they succeed and fail.
- 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.
- Clear communicator; able to articulate design with architects and discuss strategy and requirements with teams.
What You'll Contribute
- Design, develop, deploy, and support 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.
- Build feedforward guides — agent instruction files, reusable skills, architectural rules, reference docs, and codemods — that help 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 before they reach human reviewers.
- Run the steering loop — when an agent repeats a mistake, engineer a control so it can't happen again — and help keep repository knowledge (docs, specs, context) legible to agents, fighting drift.
- Contribute to quality gating and release criteria, and to LLM testing that ensures AI-generated output meets quality and safety thresholds.
- Help 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.
- Evaluate the stability, compatibility, scalability, interoperability, and performance of harness components.
- Continually learn new techniques in agent-augmented engineering, and serve as a source of technical expertise and mentor to junior team members.
Our Offer to You
- A culture and work environment strongly reflecting our core values: Act Like an Owner, Delight Our Customers and Earn the Respect of Others.
- The opportunity to make a difference by leveraging your unique strengths.
- Highly competitive compensation and rewards.
- Flexible work options, opportunities to give back to your community, social events with colleagues and a comprehensive benefits program inclusive of progressive parental leave.
Click on Apply to know more.
