Principal/ Sr Engineer- Harness Engineering at Fair Isaac in Bangalore

Job description

FICO (NYSE: FICO) is a leading global analytics software company, helping businesses in 100+ countries make better decisions. Join our world-class team today and fulfill your career potential! The Opportunity Come join our Software Engineering team in a hands-on technical role at the heart of a new discipline: Harness Engineering. As AI coding agents take on more of the software lifecycle, the hard part is no longer writing code — agents generate it faster than humans can review it, so the bottleneck shifts to verification and trust. Harness Engineering exists to break that bottleneck: engineering the environment that steers agents toward correct, maintainable, well-architected output so that quality is enforced by the system, not re-audited by a person on every change. We call that environment the harness (Agent = Model + Harness). As a Senior Harness Engineer (Band D2), you’ll independently own whole harness subsystems, set the standards other engineers build to, and be involved in the end-to-end lifecycle of turning raw model capability into production-grade engineering. What You’ll Contribute 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 — deciding where each control runs in the path to production (fast pre-commit checks, heavier post-integration checks, or continuous drift-scanning sensors) to keep quality as far left as is economical, while treating repository knowledge as the system of record and fighting drift with continuous garbage collection. 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. What We’re Seeking Bachelor’s/Master’s in Computer Science or related field, or equivalent experience in software architecture, design, development, and testing, with a strong track record in large, complex codebases. Hands-on experience with AI coding agents (e.g. Claude Code, Codex, or similar), including building your own tooling or harnesses to make AI agents genuinely effective — not just using them as a power user. Proven ability to build engineering tooling across a modern stack, including linters, static analysis, CI/CD pipelines, containerized build/test environments, and instrumentation/observability. Experience with spec-driven development, context engineering, agent orchestration, and fitness functions, with a systems mindset that favors fixing the environment over fixing individual outputs. Judgment in choo