AI Engineer - AI SDLC Tooling & Guardrails

BME | Bolsas y Mercados Españoles; Las Rozas de Madrid; Sur place; EUR 90 000 - 120 000; Plein temps

Résumé du poste

SIX is building an AI-driven SDLC at the core of its software engineering org. We are forming a team to shape guardrails that ensure agentic development is trustworthy at scale, including test automation, coverage enforcement, and independent review layers that let AI agents write and ship code without compromising quality.

This role focuses on delivering AI SDLC tooling as a platform capability, operating the agent harness, and defining governance and quality gates within CI/CD, while driving

Qualifications

• Hands-on familiarity with agentic AI architectures: agent orchestration, tool-use/sandboxing, and harness design.
• 6+ years in software engineering with deep, hands-on expertise in developer tooling or platform engineering.
• Experience evaluating LLM or agent systems (eval sets, error analysis, LLM-as-a-judge).
• Working knowledge of test methodology and code quality: test pyramids, coverage strategy, mutation testing, flaky-test remediation.

Responsabilités

• Build AI SDLC tooling as a platform capability, not a project.
• Build and operate the AI agent harness with CI/CD integration and traceability.
• Own the independent review layer for AI-generated code and tests in CI/CD.

Connaissances

Agentic AI architectures

Tool-use and sandboxing

Harness design

Test automation

Platform engineering

Tooling adoption

Governance and controls

Outils

CI/CD tooling

Description du poste

Agentic AI is changing how software gets built — and at SIX, it will touch the work of more than thousand engineers. We're building a new team at the center of this shift, responsible for shaping how AI-driven software engineering works across the company. As part of this team, you will build the guardrails that make agentic development trustworthy at scale: the test automation, coverage enforcement, and independent review layers that let AI agents write and ship code without compromising quality.

What You Will Do

• Build AI SDLC tooling as a platform capability, not a project. Its pillars are evaluation of agents, skills and prompts; AI code review in CI; and test automation: agent-driven test generation and maintenance, coverage enforcement on changed lines, flaky-test detection and self-healing, mutation testing, and test-suite health (redundancy, gaps, execution time) across unit, integration, and end-to-end layers.
• Build and operate the AI agent harness. The sandboxed execution environment, tool/permission scoping, and CI/CD integration that lets agents propose, test, and merge code, instrumented with decision logs and action provenance so every agentic change is traceable and reproducible.
• Own the independent review layer for AI-generated code and tests, including AI code review measured for missed defects and false positives: correctness, coverage, and quality gates in CI/CD that verify what an agent produces using tooling the agent doesn’t control, so speed never comes at the cost of test quality or code health.
• Define and evolve the quality and tooling strategy for an AI-driven SDLC: what gets tested where, how test data is generated and managed, how test quality is measured (not just pass/fail, but whether the tests catch regressions), and how the SDLC standards defined by the Enterprise SDLC track are enforced in tooling.
• Treat AI SDLC tooling and guardrails as a product, not a gate. Instrument for adoption, false-positive rate, and developer trust, keeping the safe, well-tested path the fastest path.

What You Bring

• Hands-on familiarity with agentic AI architectures: agent orchestration, tool-use/sandboxing, and harness design, not just conceptual awareness of “AI in the SDLC.”
• 6+ years in software engineering with deep, hands-on expertise in developer tooling or platform engineering. Test automation experience is a strong plus, not a prerequisite. Ideally someone with a track record of building tooling or developer infrastructure that other engineering teams adopted, not just writing test suites.
• Experience evaluating LLM or agent systems (eval sets, error analysis, LLM-as-a-judge) is a strong plus.
• Working knowledge of test methodology and code quality: test pyramids, coverage strategy, mutation testing, flaky-test remediation, and what makes a test suite trustworthy versus just green.
• Platform-product mindset. You build reusable capabilities and pave roads for other engineers, and you measure and strive for adoption and impact across SIX’s business units.
• Working knowledge of governance and controls in a regulated or high-assurance setting. Financial market infrastructure experience is a plus; experience in an adjacent regulated industry counts too.