Junior AI Engineer AI Code Quality Validation for T Cloud Public (m/f/d)

Odmeňovanie

From 2 000 EUR/month

Final salary is negotiable.

We are offering base salary depending on seniority level and previous experience of candidate. In addition to base salary we provide variable part and other financial benefits.

Mission

Strengthen software handover readiness by combining AI-assisted software engineering with code quality, security validation, dependency analysis, and build evidence generation for complex cloud platform repositories.

Role focus

This position focuses on trustworthy validation of large codebases. The candidate should use AI development platforms alongside static analysis, dependency scanning, build diagnostics, and expert review to identify risks in OpenStack-derived services, infrastructure code, integration scripts, and platform automation.

What will you do?

• Analyse repositories for maintainability, dependency risks, hidden coupling, insecure patterns, build fragility, licensing signals, and documentation gaps relevant to due diligence.
• Use AI coding agents to accelerate code review preparation, vulnerability explanation, remediation proposal drafting, and technical debt clustering across large codebases.
• Run and interpret quality, dependency, secret, container, and infrastructure scanning tools while documenting false positives, residual risks, and required expert review.
• Support reproducible build and release validation by analysing logs, pipeline definitions, container images, package sources, artefact flows, and configuration assumptions.
• Create evidence packs that link findings to code locations, tool results, reviewer decisions, risk severity, mitigation options, and readiness implications.
• Collaborate with security, DevOps, architecture, and test specialists to ensure AI-assisted validation results are actionable and aligned with enterprise assurance expectations.

Examples of market tools, models, and SDLC platforms expected

• AI coding and review environments.
• Open-source or internally hosted coding-capable models.
• Quality and security tooling such as static analysis, vulnerability scanning, dependency analysis, secret scanning, software bill of materials tooling, and container/image scanners.
• Engineering environments including version control, continuous integration and delivery, container orchestration, infrastructure automation, package registries, Python tooling, and log analysis workflows.

You will succeed if you: