Stanley/Stella is accelerating its digital transformation to strengthen customer experience and scale its business platforms across an international ecosystem of decorators, resellers, and partners. The Technology architect acts as the company’s senior technical authority , reporting directly to the CIO. This role defines, governs, and continuously evolves the technology foundations of the enterprise ensuring coherence, scalability, resilience, and security across all platforms, integrations, and data capabilities. This is a cross-cutting technical mandate not a functional management role with accountability for integrating, securing, and anticipating technologies across the organization. The role prevents fragmentation, avoids short-term decisions with long-term consequences, and translates emerging technologies into robust, production-ready foundations . A key and immediate priority is to lead the integration of AI technologies including RAG pipelines, agentic workflows, orchestration tools, low-code platforms, and API-based integrations across the company’s core stack: Infor M3, HubSpot, Magento, Azure, and Power BI . “Build the AI foundations of today while helping define the engineering practices of tomorrow” The Mission Turn strategic technology and AI ambitions into operational reality. Design and operationalise a vendor-agnostic AI ecosystem, help define future engineering practices, and establish the architecture, standards, governance and platform capabilities that enable sustainable innovation across Stanley/Stella. What You Will Build in the First 12 Months Enterprise AI Reference Architecture and target AI Technology Stack First production-ready Agent Platform MCP standards and API-first integration patterns AI Governance, Prompt Engineering, Evaluation and Drift Monitoring Frameworks Enterprise AI Technology Radar AI-Assisted Engineering Framework including vibe coding, application generation, testing, qualification and production readiness standards As software engineering is being transformed by AI, low-code platforms and application-generation tools, you will help define how these capabilities should be safely adopted across the organisation , including the guardrails, standards and best practices required to balance innovation, quality, security and maintainability. AI & Engineering Adoption Dashboard measuring usage, maturity, compliance and value delivery Your Impact Build a model-agnostic AI architecture Co-design AI-ready data foundations with the Data Lead Drive MCP and API-first transformation Define standards for agents, copilots, RAG and intelligent automation Guide technology choices across AI platforms, cloud services, front-end frameworks, digital experience platforms and future engineering capabilities Evaluate modern web technologies, composable architectures, React/Next.js ecosystems, AI-generated applications, low-code platforms and industrialized vibe coding approaches Define the do’s and don’ts for AI-assisted development and application generation Establish standards for automated testing, qualification, security reviews and production readiness Promote, evangelize , measure and continuously improve the frameworks you define Contribute to broader architecture, integration, observability, platform and security topics Adoption, Governance & Measurement You do not ‘only ‘define standards and frameworks. You ensure they are adopted, measurable and continuously improved. You will animate communities of practice, establish KPIs, monitor adoption, measure compliance, create feedback loops, identify improvement opportunities and report outcomes to technology leadership. Success is not achieved when standards are documented. Success is achieved when they are adopted, measured, challenged and continuously improved across the organisation. What Matters You ensure that data capabilities evolve in a way that supports AI adoption and agent-based architectures working closely with Data Lead. You have a strong appreciation for security, resilience and operational excellence. You likely started your career as a software engineer, developer or technical lead bef ore evolv ing into architecture and technology leadership roles. You understand what it takes to build, integrate, deploy and operate real-world systems because you have done it yourself. You combine hands-on technical credibility with the ability to step back, structure complex problems and design sustainable enterprise foundations. You are equally comfortable discussing APIs, integration patterns, cloud platforms, modern web technologies, front-end architectures, data architectures, AI frameworks and business priorities. You have enough software engineering experience to credibly challenge architecture decisions across front-end, back-end, integration, cloud and AI domains. Most importantly, you are pragmatic : y ou distinguish sustainable enterprise capabilities from short-lived technology trends and make recommendations based on business value, scalability, maintainability and long-term impact rather than hype. Potential: You will not be measured by the number of chatbots you build, but by your ability to establish the architecture, platforms, standards and operating models that allow Stanley/Stella to scale AI and modern engineering practices consistently across the enterprise.