Solution Architect, AI & Intelligent Automation – AI Solutions Architecture

Job Description

AI Solutions Architect | WTW

The Tone:
This is a full-time role at WTW, located in King of Prussia, Pennsylvania, USA. The company partners with internal client teams to deliver scalable and production-grade AI solutions. This role is crucial for enabling a high-performing development team, driving end-to-end solution design, and modernizing existing AI workloads. It establishes a forward-looking architecture practice built on the latest AI, integration, and cloud-native patterns for enterprise-wide impact.

The TL;DR
• Role: Full Time
• Type: Full-time
• Location: Hybrid – King of Prussia, Pennsylvania, USA
• Mission: Translate complex business requirements into scalable, production-grade architectures and modernize existing AI workloads while establishing a forward-looking AI architecture practice.
• Tech Stack: Azure (AKS, Azure OpenAI Service, Azure AI Foundry, Azure Functions, API Management, Service Bus, Azure AI Search, Cosmos DB, Azure Data Factory), Microsoft Power Platform (Power Apps, Power Automate, Copilot Studio, AI Builder), Lyzr, Moveworks, LangChain, LangGraph, OpenAI, Google Gemini, M365 ecosystem (SharePoint, Teams), Entra ID, OAuth 2.0, managed identity, Azure Data Lake, Fabric, Purview, Promptflow evals, LangSmith, Python, Node.js/.NET, Azure Monitor, ragas.

What You’ll Actually Do
• Client & Discovery: Partner directly with internal clients to deeply understand their use cases, identify core problems, define success criteria, and translate these requirements into a clearly scoped Minimum Viable Product (MVP), acting as the technical voice to bridge business needs with technical possibilities.
• Solution Architecture: Design and own end-to-end architectures for AI solutions across the full delivery spectrum, including pro-code applications on Azure, low-code solutions on Power Platform and Copilot Studio, and third-party platforms, producing necessary architecture artefacts like HLDs, LLDs, and ADRs.
• AI & Agentic Design: Lead the architecture of advanced AI capabilities such as multi-agent systems, agentic workflows, advanced RAG (contextual retrieval, hybrid search, re-ranking), MCP integration, and next-generation AI orchestration patterns using frameworks like Azure AI Foundry and LangGraph.
• Workload Modernization: Assess and evolve current LangChain/LangGraph and OpenAI-based pipelines and Google Cloud AI assets, defining a roadmap to advance them toward production-grade, observable, and maintainable architectures aligned with enterprise standards.
• Observability & Excellence: Embed observability-first thinking into every architecture, defining logging, tracing, evaluation, and monitoring frameworks for AI systems using tools such as Azure Monitor, Promptflow evals, or LangSmith, ensuring solutions are auditable and trustworthy at scale.
• Developer Enablement: Work hands-on with the engineering team as a trusted design partner, conducting architecture reviews, providing guidance during delivery, establishing reusable patterns and reference architectures, and reducing technical debt through principled design decisions.

The Must-Haves
• Background: Possess 7+ years of solution or cloud architecture experience, with a strong preference for Azure and demonstrated expertise in designing cloud-native, enterprise-scale applications and familiarity with Well-Architected Framework principles.
• Experience: Proven hands-on experience designing and deploying production RAG systems, deep knowledge of advanced RAG patterns (hybrid search, re-ranking, graphRAG), hands-on experience with LangChain, LangGraph, and agentic orchestration patterns, and experience with LLM providers like Azure OpenAI and Google Gemini.
• Skills: Architecture-level knowledge of the Microsoft Power Platform (Power Apps, Power Automate, Copilot Studio, AI Builder), hands-on experience designing backend services in Python and/or Node.js/.NET, and familiarity with enterprise application integration including M365 ecosystem and identity/security solutions.
• Bonus: Experience designing or advising on the architecture around predictive ML models, specifically understanding data pipelines, feature stores, and model serving in an enterprise context.