AI Solutions Architect

Description

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.

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.

Qualifications

The Requirements

Cloud & Infrastructure architecture

• 7+ years of solution or cloud architecture experience; strong preference for Azure (AKS, Azure OpenAI Service, Azure AI Foundry, Azure Functions, API Management, Service Bus, Azure AI Search, Cosmos DB, Azure Data Factory). Equivalent GCP or AWS considered.
• Demonstrated experience designing cloud-native, enterprise-scale applications.
• Familiarity with Well-Architected Framework principles (reliability, security, cost optimization, operational excellence).

AI, ML & agentic systems

• Proven hands-on experience designing and deploying production RAG systems. Deep knowledge of advanced RAG patterns: hybrid search, re-ranking, contextual chunking, graphRAG, and long-context strategies. Understanding of MCP (Model Context Protocol) as an emerging integration pattern.
• Hands-on experience with LangChain, LangGraph, and agentic orchestration patterns (ReAct, Plan-and-Execute, multi-agent supervisor patterns).
• Experience working with LLM providers: Azure OpenAI, Google Gemini, and open-weight models via Azure AI Foundry model catalogue.
• Familiarity with AI safety, responsible AI principles, and enterprise guardrail patterns (content filtering, grounding checks). Experience designing AI evaluation frameworks (ragas, offline evals, online monitoring, LLM-as-judge).
• Nice to have: Experience designing or advising on predictive ML models (classification, forecasting) — not necessarily model training, but understanding the architecture around data pipelines, feature stores, and model serving in an enterprise context.

Low-code, automation & platform tools

• Architecture-level knowledge of the Microsoft Power Platform: Power Apps, Power Automate, Copilot Studio (formerly PVA), and AI Builder.
• Exposure to enterprise third-party AI platforms such as Lyzr (agent builder), Moveworks (enterprise AI assistant), or comparable (ServiceNow AI, Glean, Workato). Ability to assess fit-for-purpose versus build vs. buy for AI automation scenarios.

Backend, integration & software architecture

• Hands-on experience designing backend services in Python and/or Node.js/.NET.
• Familiarity with enterprise application integration: M365 ecosystem (SharePoint, Teams), identity & security (Entra ID, OAuth 2.0, managed identity), and data platforms (Azure Data Lake, Fabric, Purview) as AI data sources.

Communication & ways of working

• Exceptional ability to communicate complex technical concepts to non-technical senior stakeholders.
• Collaborative mindset with experience guiding and mentoring engineering teams

WTW is an Equal Opportunity Employer