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

Mission
Strengthen Meridian 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 such as Cursor, Windsurf, Claude Code, Continue, Cline, Aider, or VS Code-based assistants connected to approved model endpoints.
• Open-source or Chinese coding-capable models such as DeepSeek Coder, Qwen/Qwen-Coder, CodeGeeX, StarCoder, Code Llama, Mistral, or similar internally hosted models.
• Quality and security tooling such as SonarQube, Semgrep, Trivy, GitGuardian, Syft, Grype, dependency-check, Falco, SBOM tooling, and container/image scanners.
• Engineering environments including GitLab, GitHub Enterprise, Jenkins, Kubernetes, Helm, Docker, ArgoCD, package registries, Python tooling, and log analysis workflows.