AI Solutions Architect
Job in
Indiana Borough, Indiana County, Pennsylvania, 15705, USA
Listed on 2026-08-08
Listing for:
Socket.dev
Full Time
position Listed on 2026-08-08
Job specializations:
-
Software Development
AI Engineer (Applied/Software), Software Architect, Cloud Engineer - Software, Azure
Job Description & How to Apply Below
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 Lang Chain/Lang Graph pipelines and RAG systems - while establishing a forward-looking architecture practice built on the latest AI, integration, and cloud-native patterns.
- 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, Lang Graph, and adjacent frameworks.
- Modernization of existing AI workloads — Assess and evolve current Lang Chain/Lang Graph 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, Service Now, 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, Prompt flow evals, Lang Smith, 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.
- 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).
- 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 Lang Chain, Lang Graph, and agentic orchestration patterns (ReAct, Plan-and-Execute, multi-agent supervisor patterns).
- Experience working with LLM providers:
Azure OpenAI, Google Gemini, and open-weight…
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