The Project: Internal Support Tools Evolution   You are joining an initiative focused on the evolution, modernization of the Internal Application Landscape. This project involves the strategic transition of well-known cloud services and legacy .NET architectures into a next-generation, AI-augmented, and cloud-native software landscape.  Our goal is not simply to "maintain" the existing system, but to proactively transform it—ensuring sovereignty, high-performance scalability, and seamless handover readiness through disciplined engineering.  Our Engineering Philosophy  We operate at the intersection of Legacy Stability and Modern Innovation. Our approach is defined by:  AI-Augmented Engineering: We utilize advanced AI development environments (such as Cursor, Windsurf, and Claude Code) and cutting-edge model families to accelerate code comprehension, refactoring, and documentation.  Disciplined SDLC Patterns: We move beyond traditional development by implementing repeatable, evidence-based SDLC patterns. Every change is backed by structured engineering evidence and rigorous automated testing.  The "Strangler Fig" Modernization: We prioritize risk-mitigated evolution, using sophisticated patterns to wrap, refactor, and eventually replace legacy components.  High-Seniority Autonomy: We operate as a lean, highly autonomous unit. We value "technical detectives" who can navigate ambiguity and perform deep reverse engineering.  Mission To lead the evolution of the Internal Application Landscape by designing repeatable SDLC patterns that bridge the gap between legacy OpenStack-derived services and next-generation AI-assisted software engineering. You will ensure that the transition from legacy architectures to modern, AI-integrated platforms delivers maximum business value while maintaining rigorous engineering evidence.  Role Focus This role anchors the engineering approach for repository-scale understanding and sovereign software handover. You will focus on defining AI-assisted SDLC patterns for repository intake, service mapping, and engineering evidence generation, ensuring that coding agents and open-weight models are used to accelerate refactoring and documentation without compromising confidentiality.  Key Responsibilities  AI-Assisted SDLC Pattern Definition: Lead the design of patterns for repository intake, code exploration, build diagnosis, and engineering evidence generation using AI-driven workflows.  Strategic Roadmap & Prioritization: Manage and prioritize the Product Backlog, making critical decisions on the balance between "Run" (maintaining OpenStack-derived services) and "Change" (modernizing via AI-assisted refactoring).  AI Model Evaluation: Guide the evaluation and operationalization of AI development platforms and approved coding model stacks (e.g., DeepSeek, Qwen, Mistral) for secure enterprise use cases.  Stakeholder Orchestration & Handover: Produce senior stakeholder-ready artifacts, including transition risks, service decomposition views, and structured evidence templates for sovereign software handover.  Human-in-the-loop Governance: Establish quality gates, prompt libraries, and reusable review patterns to validate AI-generated engineering outputs.  Rapid Prototyping: Utilize Microsoft PowerApps to quickly prototype business logic workflows and validate requirements with stakeholders.