AI Engineering- Lead (AI Coding Agent, Platform Engineering, Loop Engineering, AWS)

FICO; Bengaluru; Hybrid; INR 3,500,000 - 7,000,000; Full time

Benefits offered by this job

Flexible work options

Benefits program

Parental leave

Social events

Job summary

FICO is seeking a Lead Harness Engineer to own the harness, from problem framing to production. You will build the environment that guides AI agents, ensuring quality through automated rules and tests.

The role emphasizes systems thinking, architecture, and maintainability, with hands-on work across the stack. You will design guide and feedback systems, push for robust tests, and collaborate with architects to shape the developer experience.

Qualifications

• Bachelor's or Master's in CS or related field, or equivalent experience.
• 7+ years of software/ platform engineering in large codebases.
• Experience with modern tooling: linters, CI pipelines, containers, instrumentation.
• 2+ years with AI coding agents and understanding their strengths/weaknesses.
• Experience with spec-driven development and developer-platform work.
• Ability to encode design rules into repeatable automation.

Responsibilities

• Design, develop, deploy, and support harness components and guardrails.
• Build feedforward guides: instruction files, skills, documentation, codemods.
• Create feedback sensors: linters, static analysis, tests, and LLM-reviewers.
• Implement steering loop to prevent repeated mistakes and maintain knowledge.
• Contribute to quality gates, release criteria, and AI testing for safety.
• Improve observability and track key metrics like PR velocity and defect escape rate.

Skills

Software architecture

Testing & reliability

CI/CD pipelines

AI coding agents

Observability

System design

Education

Bachelor's/Master's in CS

Tools

Docker

Kubernetes

Git

CI/CD

Job description

**Location: This role would require you to work from Bangalore, India office (3days/ week)

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.
• 7+ years strong software engineering/ platform 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.
• 2+ years of 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.