Lead AI Solutions Architect - AI Platform, Agents, RAG & Automation

Job Summary

• Define and lead the architecture of an Enterprise AI Platform covering AI Agents, RAG, LLMs, workflows, process automation, and enterprise tool integration
• Provide technical leadership while remaining hands-on with critical architecture and prototypes

Responsibilities

• Define AI platform architecture, standards, and technical roadmap
• Design scalable AI Agent, Agentic AI, and multi-agent architectures
• Define enterprise RAG and knowledge architecture
• Design AI workflow and business process automation architecture
• Define reusable connector, plugin, and tool architecture
• Architect integrations with Microsoft 365, Outlook, Teams, SharePoint, Jira, Git, Slack, databases, and enterprise applications
• Define LLM strategy covering cloud, private, and open-source models
• Establish AI security, governance, access control, and compliance
• Define AI evaluation, observability, monitoring, and auditability
• Lead architecture reviews, POCs, technology selection, and mentor AI engineers

Required Skills

• Architecture: Enterprise Architecture, Solution Architecture, Distributed Systems, Microservices, Event-Driven Architecture, Scalability, Security
• AI/LLM: Generative AI, LLMs, RAG, AI Agents, Agentic AI, Function Calling, Tool Calling
• Agents: Agent Orchestration, Multi-Agent Systems, Planning, Reasoning, Memory, Human-in-the-Loop
• Frameworks: LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI
• RAG/Data: Vector Databases, Embeddings, Semantic/Hybrid Search, Reranking, Knowledge Graphs
• LLM Infrastructure: OpenAI, Azure OpenAI, Anthropic, Gemini, Open-Source LLMs, vLLM, llama.cpp, Ollama
• Enterprise Integration: Microsoft Graph, Microsoft 365, Outlook, Teams, SharePoint, Jira, Git, Slack, REST APIs, OAuth2, Webhooks, MCP
• Engineering: Python, APIs, Git, CI/CD, Docker, Kubernetes, Azure/AWS/GCP
• Security/Governance: AI Security, Prompt Injection, Data Privacy, RBAC, Agent Security, Audit, Responsible AI
• Experience: 10+ years software engineering/architecture with significant hands-on GenAI/LLM experience
• Education: Bachelors/Masters in Computer Science, Engineering, AI, Data Science, IT, or equivalent

Ideal Candidate

• Hands-on AI Architect capable of defining LLMs + RAG + Agents + Workflows + Automation + Connectors + Tools + Security + Governance
• Proven technical leader who can architect the platform and guide engineering teams to production

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