Job ID: 744000142834900 AI SWE / Code Quality Validation Engineer (m/f/d)Granada Professional Regular T-Systems Iberia Your Task This role focuses on analyzing repositories and codebases for maintainability, security risks, and technical debt while leveraging AI coding agents to accelerate code review and validation processes. You will support due diligence activities by running quality and security scanning tools, validating build and release processes, and creating comprehensive evidence packs that document findings and risk assessments.

Key Responsibilities
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, Dev Ops, architecture, and test specialists to ensure AI-assisted validation results are actionable and aligned with enterprise assurance expectations Tools and Platforms You will work with AI coding and review environments such as Cursor, Windsurf, Claude Code, Continue, Cline, Aider, or VS Code-based assistants connected to approved model endpoints.



You will utilize open-source or Chinese coding-capable models such as Deep Seek Coder, Qwen/Qwen-Coder, Code Gee X, Star Coder, Code Llama, Mistral, or similar internally hosted models. Quality and security tooling includes Sonar Qube, Semgrep, Trivy, Git Guardian, Syft, Grype, dependency-check, Falco, SBOM tooling, and container/image scanners. Engineering environments include Git Lab, Git Hub Enterprise, Jenkins, Kubernetes, Helm, Docker, Argo CD, package registries, Python tooling, and log analysis workflows.

Your Profile
We are seeking a highly skilled Software Engineer with extensive experience in secure development, Dev Sec Ops, and platform engineering, combined with strong expertise in AI-assisted code review, vulnerability analysis, and software supply-chain security validation. Must Have Skills5+ years of experience in software engineering, secure development, Dev Sec Ops, platform engineering, or quality-focused engineering roles Strong Python programming skills Practical experience in at least one backend or systems language such as Go, Java, C, C++, or Rust Hands-on experience with code review Hands-on experience with vulnerability triage Hands-on experience with dependency analysis Hands-on experience with CI/CD inspection Hands-on experience with container security Hands-on experience with software supply-chain evidence generation Practical experience using AI coding assistants for code understanding, documentation, remediation proposals,



test generation, or large-repository review acceleration Ability to analyze repositories for maintainability, dependency risks, hidden coupling, insecure patterns, build fragility, licensing signals, and documentation gaps relevant to due diligence Ability to use AI coding agents to accelerate code review preparation, vulnerability explanation, remediation proposal drafting, and technical debt clustering across large codebases Ability to run and interpret quality, dependency, secret, container, and infrastructure scanning tools while documenting false positives, residual risks, and required expert review Ability to support reproducible build and release validation by analyzing logs, pipeline definitions, container images, package sources, artifact flows, and configuration assumptions Ability to create evidence packs that link findings to code locations, tool results, reviewer decisions, risk severity, mitigation options, and readiness implications Ability to collaborate with security, Dev Ops, architecture, and test specialists to ensure AI-assisted validation results are actionable and aligned with enterprise assurance expectations Comfortable producing concise, evidence-backed findings in high-accountability environments where confidentiality, auditability, and reviewer judgement are essential Experience with AI coding and review environments such as Cursor, Windsurf, Claude Code, Continue, Cline, Aider, or VS Code-based assistants connected to approved model endpoints Experience with open-source or Chinese coding-capable models such as Deep Seek Coder, Qwen/Qwen-Coder, Code Gee X, Star Coder, Code Llama, Mistral, or similar internally hosted models Experience with quality and security tooling

📌 Ai swe (Madrid)
🏢 Telekom
📍 Madrid