AI Solutions Architect

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    "ExternalDescriptionStr": "<p>You will partner with IT Director and internal client teams to translate complex business requirements into scalable, production-grade architectures — spanning pro-code Azure solutions, low-code Power Platform experiences, and emerging agentic AI frameworks. This role is the keystone that unblocks a high-performing development team by owning end-to-end solution design: from initial client discovery and MVP scoping through to architecture governance, observability strategy, and developer guidance. You will modernize existing AI workloads — including LangChain/LangGraph pipelines and RAG systems — while establishing a forward-looking architecture practice built on the latest AI, integration, and cloud-native patterns.</p><p><strong>The Role&nbsp;</strong></p><ul style=\"list-style-type: disc;\"><li>Client engagement &amp; discovery — Work directly with internal clients to deeply understand their use cases, identify the core problem and success criteria, and translate requirements into a clearly scoped MVP. Act as the technical voice in stakeholder conversations, bridging business need to technical possibility.</li><li>Solution architecture ownership — Design and own end-to-end architectures for AI solutions across the full delivery spectrum: pro-code applications on Azure, low-code solutions on Power Platform &amp; Copilot Studio, and third-party platforms such as Lyzr or Moveworks. Produce architecture artefacts (HLD, LLD, ADRs) that guide delivery teams.</li><li>AI &amp; agentic framework design — Lead the architecture of advanced AI capabilities: multi-agent systems, agentic workflows, advanced RAG (contextual retrieval, hybrid search, re-ranking), MCP integration, and next-generation AI orchestration patterns using Azure AI Foundry, LangGraph, and adjacent frameworks.</li><li>Modernization of existing AI workloads — Assess and evolve current LangChain/LangGraph and OpenAI-based pipelines and Google Cloud AI assets. Define a roadmap to advance these toward production-grade, observable, and maintainable architectures aligned with enterprise standards.</li><li>Backend &amp; integration architecture — Design scalable APIs, event-driven integrations, and enterprise connectors that underpin AI solutions. Ensure AI capabilities integrate cleanly with enterprise systems (M365, ServiceNow, ERP, HR platforms, etc.).</li><li>Observability &amp; operational excellence — Embed observability-first thinking into every architecture: define logging, tracing, evaluation, and monitoring frameworks for AI systems using tools such as Azure Monitor, Promptflow evals, LangSmith, or equivalent. Ensure AI solutions are auditable and trustworthy at scale.</li><li>Developer enablement &amp; technical governance — Work hands-on with the engineering team as a trusted design partner. Conduct architecture reviews, provide hands-on guidance during delivery, establish reusable patterns and reference architectures, and reduce technical debt through principled design decisions.</li><li>Technology radar &amp; innovation — Maintain an active awareness of the AI tooling landscape. Evaluate and recommend emerging platforms, frameworks, and patterns that could improve delivery speed, capability, or cost-efficiency for the team.</li></ul>",
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