AI Engineer - AI SDLC Tooling & Guardrails

SIX; Las Rozas de Madrid; Hybrid; EUR 70,000 - 110,000; Full time

Job summary

In this role, you will own the AI SDLC tooling platform, implement for adoption, monitor false positives, and ensure alignment with the Enterprise SDLC standards across SIX’s business units. This position offers a hybrid model with on-site collaboration and remote work options.

Qualifications

• 6+ years in software engineering with deep, hands-on expertise in developer tooling or platform engineering.
• Experience evaluating LLM or agent systems is a strong plus.
• Familiarity with test methodology, code quality, and test pyramids.

Responsibilities

• Build AI SDLC tooling as a platform capability, including agent evaluation, prompts, and test automation.
• Build and operate the AI agent harness with sandboxed execution and CI/CD integration.
• Own the independent review layer for AI-generated code and tests in CI/CD pipelines.
• Define and evolve the quality and tooling strategy for the AI-driven SDLC.
• Treat AI SDLC tooling as a product, measuring adoption and impact.

Skills

Agentic AI

Developer tooling

Platform engineering

Test automation

LLM evaluation

CI/CD tooling

Tools

CI/CD tooling

Sandbox environments

Mutation testing tools

LLM evaluation frameworks

Job description

Madrid, Warsaw | Working from home up to 40% | Referencia 8247

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.