Senior Architect - .Net Agentic AI

Job description

Job Summary Senior Software Architect - Senior AI Software Engineering Expert. We are looking for a highly experienced software engineering expert who combines strong architecture and implementation skills with practical, disciplined use of AI-assisted development tools. This is not a generic "prompting" role. We need someone who can work across architecture, implementation, review, testing, and delivery, while also training other engineers to use AI in a controlled, repeatable, and production-ready way. The successful candidate will help teams move beyond ad hoc AI usage. They will introduce and coach engineers on a structured AI-assisted engineering workflow that includes planning before implementation, clear constraints, reusable prompting patterns, visible review, validation, and traceability from requirements to delivered code. You should be equally comfortable writing production code, reviewing system design, improving developer workflows, and coaching engineers on how to use tools such as Cursor, GitHub Copilot, Claude Code, Cline, or similar platforms responsibly and effectively. Responsibilities - Lead hands-on software engineering work across design, implementation, refactoring, testing, and delivery for cloud-based business applications. - Shape solution architecture for scalable, secure, maintainable platforms, with a strong focus on .NET, Azure, APIs, and distributed systems. - Define and roll out a practical AI-assisted engineering methodology that teams can reuse across projects. - Train engineers to use AI with discipline, not just speed, including how to plan work, constrain execution, review outputs, validate results, and maintain traceability. - Create reusable prompts, templates, working agreements, commands, or playbooks that reduce inconsistent one-off prompting. - Coach teams on how to break work down into structured execution flows such as feature-by-feature, layer-by-layer, or parallelised implementation where appropriate. - Review AI-generated plans, code, and artifacts for correctness, maintainability, architecture fit, naming, file placement, dependencies, error handling, and operational impact. - Establish quality checks so AI-assisted output is validated through tests, reviews, and requirement-based verification rather than accepted at face value. - Support modernisation initiatives, including migration from legacy solutions to modern cloud-native platforms. - Contribute to DevOps, CI/CD, observability, operational readiness, and engineering governance. - Help teams adopt a delivery model where AI usage is reviewable, explainable, and aligned with enterprise standards. AI Engineering Expectations for This Role - You must be able to teach and model a controlled AI-SDLC approach, including: - Planning before implementation rather than jumping straight into code generation. - Iterative refinement of plans based on review and feedback. - Clear constraints for AI execution, including requirements, architecture decisions, coding rules, file references, and expected outputs. - Use of reusable prompts, commands, skills, or playbooks instead of relying on informal ad hoc instructions. - Structured execution strategies that make AI work easy to follow and review. - Visible review of AI-generated code and artifacts, with clear reasoning about what is accepted, rejected, or changed. - Validation through tests, automated checks, and requirement-level inspection. - Traceability between requirements, plan items, files changed, and implementation outcomes. Key Responsibilities This role requires someone who can train others in these practices, inspect whether they are being followed, and raise the maturity of the engineering organisation over time. - Deliver high-quality production code and architecture contributions in a hands-on capacity. - Partner with product, architecture, and engineering teams to define practical AI-enabled ways of working. - Run workshops, coaching sessions, and pairing sessions to train engineers on effective AI-assisted software delivery. - Create examples, reference implementations, and reusable artifacts that teams can adopt directly. - Evaluate AI development tools, workflows, and guardrails through technical proof-of-concepts. - Define what good looks like for AI-generated plans, code reviews, validation evidence, and traceability outputs. - Review and improve engineering practices covering design quality, coding standards, testing, deployment readiness, and maintainability. - Support observability, monitoring, automation, and operational readiness as part of the full delivery lifecycle. - Help teams make sound technical decisions and retain human control over important architectural and product-impacting choices. Required Experience - 14+ years of experience in software development, technical architecture, or full-stack engineering. - Strong hands-on experience with .NET Core / .NET, C#, Azure, and cloud-native application design. - Experience designing and delivering distributed systems, API-based solutions, and integration-heavy platforms. Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.