AI Solutions Architect

Core Responsibilities AI Solution Design & Delivery Translate client and internal business requirements into feasibility-validated, buildable AI solutions — delivered, not just proposed. Design and deliver end-to-end AI solutions, including agentic workflows, AI integrations, automation pipelines, and LLM-powered applications for internal and external customers. Package delivered solutions into documented, reusable assets that can be applied across engagements. Engage directly with US-based clients in discovery sessions, workshops, and solution reviews as the technical AI subject matter expert. Agentic AI, Automation & Workflow Design and build multi-agent AI systems using LangChain, LangGraph, CrewAI, or AutoGen. Architect MCP (Model Context Protocol) integrations connecting AI agents to enterprise tools, data sources, and business workflows. Build agentic automation that executes real, multi-step business processes with appropriate human-in-the-loop oversight. Apply prompt engineering, RAG pipelines, and vector database integrations to deliver accurate, context-aware AI solutions at production scale. AI Integration & Application Modernization Design AI-assisted integration solutions that connect disparate systems, data sources, and platforms — platform-agnostic, applicable across diverse client technology environments. Apply AI approaches to accelerate application modernization, legacy migration, and systems integration across client technology stacks. Build reusable AI accelerators for code analysis, migration planning, and integration mapping that can be deployed across engagements. Organizational AI Adoption & Enablement Support internal teams and external clients in adopting and operationalizing AI, from readiness assessment through practical implementation and change enablement. Design AI guardrails and governance practices, including data handling, human-in-the-loop controls, and responsible AI use that are appropriate for client-facing agentic systems. Build AI solutions that improve operational efficiency for both client organizations and Integrative's own internal teams and functions. Develop practical adoption frameworks, runbooks, and enablement materials that help non-AI practitioners use and sustain delivered solutions. Recruit and mentor junior AI engineers; collaborate with Integrative's enterprise architecture group to ensure AI solutions align with broader technology standards. Continuously monitor emerging AI tools, frameworks, and platform releases; evaluate applicability and bring relevant recommendations forward. Required Skills & Competencies Agentic AI & Orchestration LangChain and LangGraph — hands-on production experience required. MCP (Model Context Protocol) design and implementation required. Multi-agent frameworks (CrewAI, AutoGen, or equivalent) strongly preferred. Tool calling, memory management, context and state handling, RAG pipeline architecture. AI…