Job Description

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, 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: Cursor, Windsurf, Claude Code, Continue, Cline, Aider, or VS Code-based assistants connected to approved model endpoints.

• Open-source or Chinese coding-capable models: DeepSeek Coder, Qwen/Qwen-Coder, CodeGeeX, StarCoder, Code Llama, Mistral, or similar internally hosted models.

• Quality and security tooling: SonarQube, Semgrep, Trivy, GitGuardian, Syft, Grype, dependency-check, Falco, SBOM tooling, and container/image scanners.

• Engineering environments: GitLab, GitHub Enterprise, Jenkins, Kubernetes, Helm, Docker, ArgoCD, package registries, Python tooling, and log analysis workflows.

Qualifications

• 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.

Additional Information

What do we offer you?

Work environment & flexibility

• International, dynamic and collaborative environment.

• T-Social: social initiatives (sports, community, health, ...).

• Hybrid work model (remote/on-site).

• Flexible working hours.

Growth & development

• Customized training: access to Coursera to learn whatever you want, whenever you want.

• Weekly language classes (English & German).

• International Mentoring Sessions & Experience Days.

Compensation & benefits

• Flexible compensation plan (health insurance, meal vouchers, childcare, transport).

• Telemedicine.

• Life and accident insurance.

• Social fund.

Wellbeing & time off

• 26+ working days of vacation per year.

• Free access to specialists services (medical, legal, wellness).

• 100% salary coverage during medical leave.

• And many more advantages of being part of T-Systems!

If you are looking for a new challenge, do not hesitate to send us your CV! Please send CV in English. Join our team! T-Systems Iberia will only process the CVs of candidates who meet the requirements specified for each offer.

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, DevOps, 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 DeepSeek Coder, Qwen/Qwen-Coder, CodeGeeX, StarCoder, Code Llama, Mistral, or similar internally hosted models. Quality and security tooling includes SonarQube, Semgrep, Trivy, GitGuardian, Syft, Grype, dependency-check, Falco, SBOM tooling, and container/image scanners. Engineering environments include GitLab, GitHub Enterprise, Jenkins, Kubernetes, Helm, Docker, ArgoCD, package registries, Python tooling, and log analysis workflows.

Your Profile

We are seeking a highly skilled Software Engineer with extensive experience in secure development, DevSecOps, and platform engineering, combined with strong expertise in AI-assisted code review, vulnerability analysis, and software supply-chain security validation.

Must Have Skills

• 5+ years of experience in software engineering, secure development, DevSecOps, 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, DevOps, 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 DeepSeek Coder, Qwen/Qwen-Coder, CodeGeeX, StarCoder, Code Llama, Mistral, or similar internally hosted models

• Experience with quality and security tooling such as SonarQube, Semgrep, Trivy, GitGuardian, Syft, Grype, dependency-check, Falco, SBOM tooling, and container/image scanners

• Experience with engineering environments including GitLab, GitHub Enterprise, Jenkins, Kubernetes, Helm, Docker, ArgoCD, package registries, Python tooling, and log analysis workflows

Nice to Have Skills

• Good understanding of OpenStack-derived cloud platforms

• Good understanding of distributed services

• Good understanding of Kubernetes

• Good understanding of Linux

• Good understanding of enterprise build and release processes