AI Software Engineer / Code Quality Validation Engineer

• 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. 
• 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.

Examples of market tools, models, and SDLC platforms expected 

• 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.