AI Solutions Architect

The Role

• Client engagement & 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.

• 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 & Copilot Studio, and third-party platforms such as Lyzr or Moveworks. Produce architecture artefacts (HLD, LLD, ADRs) that guide delivery teams.

• AI & 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.

• 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.

• Backend & 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.).

• Observability & 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.

• Developer enablement & 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.

• Technology radar & 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.