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FICO

Predictive credit risk scoring & analytics

Principal Engineer Agentic Development

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

About the Job

Requirements

  • Demonstrable experience in software engineering within large, complex codebases, with a strong foundation in software architecture, testing practices, and long-term maintainability.
  • Practical experience working with AI coding agents such as Claude Code, GitHub Copilot, Codex, or Cursor, with a clear understanding of their capabilities, limitations, and appropriate use within a production engineering workflow.
  • The technical judgement to critically evaluate agent-generated output, identify subtle errors or architectural weaknesses, and make informed decisions about when to intervene in the agent workflow.
  • The ability to communicate technical requirements with precision, producing specifications and definitions of done that are unambiguous, testable, and actionable by both agents and engineering colleagues.
  • A disciplined approach to verification, questioning correctness, building appropriate checks and test coverage, and applying particular rigour in regulated and mission-critical delivery contexts.
  • A results-oriented approach to delivery, with accountability for the quality and correctness of shipped work rather than volume of code produced.
  • Familiarity with relevant technologies including Python, SQL, JavaScript/TypeScript, AWS or Azure, Kubernetes, CI/CD pipelines, ML model deployment, or real-time decisioning systems.

Responsibilities

  • Collaborate with Product teams to translate early-stage ideation into structured Proofs of Concept, establishing clear technical feasibility and scope.
  • Produce detailed specifications and acceptance criteria that enable coding agents to generate correct, maintainable, and well-architected output, taking full ownership of the resulting code through to deployment.
  • Apply rigorous quality standards to all agent-generated output, ensuring it meets the same level of scrutiny as hand-written code within a regulated decisioning context.
  • Operate effectively within the agentic harness, utilising established guides, including AGENTS.md files, architectural rules, and reference documentation alongside automated sensors such as linters, fitness tests, verification loops, and LLM-as-judge reviewers.
  • Identify the root cause of recurring agent errors and work with the harness team to implement systemic controls, ensuring the same class of failure cannot recur across the codebase.
  • Maintain the accuracy and currency of repository documentation, specifications, and contextual knowledge as a shared system of record for human engineers and agents.
  • Apply focused technical judgement to architecture decisions, edge-case analysis, latency and integration risk, auditability requirements, and determinations outside the appropriate scope of agent autonomy.

Desired Qualifications

  • Experience with spec-driven development, context engineering, or agent orchestration in a team environment; knowledge of regulated or audited software delivery processes and controlled change management.
  • A background in decisioning, credit risk, fraud detection, analytics, optimisation, or data engineering.
  • A considered approach to human-AI collaboration, including delegation boundaries, human oversight, and earning autonomy through demonstrated quality.

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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