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

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Lead AI Solutions Architect - AI Platform, Agents, RAG & Automation (Chennai)
🏢 Code Work It Solutions
📍 Chennai