AI SWE / Code Quality Validation Engineer - T Cloud Public

Company Description As Hungary’s most attractive employer in 2025 (according to Randstad’s representative survey), Deutsche Telekom IT Solutions is a subsidiary of the Deutsche Telekom Group. The company provides a wide portfolio of IT and telecommunications services with more than 5300 employees. We have hundreds of large customers, corporations in Germany and in other European countries. DT-ITS recieved the Best in Educational Cooperation award from HIPA in 2019, acknowledged as the the Most Ethical Multinational Company in 2019. The company continuously develops its four sites in Budapest, Debrecen, Pécs and Szeged and is looking for skilled IT professionals to join its team. Job Description 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. 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. * Please be informed that our remote working possibility is only available within Hungary due to European taxation regulation.
Requirements:
Candidate profile 5+ years in software engineering, secure development, DevSecOps, platform engineering, or quality-focused engineering roles. Strong Python skills plus practical experience in at least one backend or systems language such as Go, Java, C, C++, or Rust. Hands-on experience with code review, vulnerability triage, dependency analysis, CI/CD inspection, container security, or software supply-chain evidence generation. Practical experience using AI coding assistants for code understanding, documentation, remediation proposals, test generation, or large-repository review acceleration. Good understanding of OpenStack-derived cloud platforms, distributed services, Kubernetes, Linux, and enterprise build and release processes is strongly preferred. Comfortable producing concise, evidence-backed findings in high-accountability environments where confidentiality, auditability, and reviewer judgement are essential.
Must have: Python, Security, AI, Cloud platform, Kubernetes, Linux, Software engineering, CI/CD, OpenStack
Nice to have: Go, C, Java, C++, Rust
Daily tasks: - 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.