AI Software Development Lifecycle Lead Engineer - T Cloud Public (REF5733O)

Job Description Mission Lead AI-assisted software engineering activities for Meridian by designing repeatable SDLC patterns that helpanalyse,validate, document, and evolve complex cloud software stacks. The role anchors the engineering approach for OpenStack-derived services, repository-scale understanding, and sovereign software handover readiness. Role focus This senior position focuses on turning AI development platforms into disciplined engineering capability. The candidate should guide how coding agents, open-source or open-weight coding models, and enterprise-approved toolchains are used to accelerate code understanding, refactoring support, documentation, and transition readiness without weakening evidence quality or confidentiality. Key responsibilities Define and lead AI-assisted SDLC patterns for repository intake, code exploration, service mapping, build diagnosis, documentation, and engineering evidence generation. Guideanalysis of OpenStack-derived control-plane services, APIs, dependencies, integration flows, and build structures across multiple repositories and teams. Evaluate and operationalize AI development platforms, coding agents, and approved open-weight or Chinese coding model stacks for secure enterprise use cases. Establish human-in-the-loop controls, quality gates, prompt libraries, reusable review patterns, and evidence standards for AI-generated engineering outputs. Coach software engineers and platform specialists on safe, effective use of AI tools while coordinating with architecture, security, DevOps, and testing teams. Produce senior stakeholder-ready artefacts including transition risks, service decomposition views, codebase maturity observations, and recommended engineering actions. Examples of market tools, models, and SDLC platforms expected AI development environments such as Cursor, Windsurf, Claude Code, Continue, Cline, or VS Code-based extensions connected to enterprise-approved model endpoints. Open-source, open-weight, or Chinese coding-capable models such as DeepSeek Coder, Qwen/Qwen-Coder,CodeGeeX,StarCoder, Code Llama, Mistral, or similar modelsoperatedthrough approved controls. Agentic and SDLC workflow components such asLangGraph, OpenAI Agents SDK,AutoGen,LlamaIndex, RAG pipelines, evaluation loops, and structured tool-calling patterns. Engineering environments including GitHub Enterprise, GitLab, Jenkins,ArgoCD, Helm, Docker, Kubernetes, private package registries, and terminal-native automation workflows.