AI Architect - RAG & Agentic AI
Hello, Spiceorb is looking for AI Architect - RAG & Agentic AI Role: AI Architect – Agentic AI & RAG Location: Remote Duration: 12+ Months Contract Job Summary We are looking for a highly skilled, hands-on AI Architect with deep expertise in Agentic AI, Multi-Agent Systems, RAG architecture, and enterprise AI solutions. The ideal candidate should have strong Python development skills, a solid foundation in data architecture, and proven experience building and deploying AI agents from scratch. They also should have Snowflake and Databricks experience.
Key Responsibilities
Design and build enterprise-grade Agentic AI solutions, including conversational AI, autonomous agents, and multi-agent systems. Architect and implement advanced RAG pipelines, vector search, embeddings, retrieval strategies, and context management. Develop AI agents from scratch using Python, LangGraph, LangChain, Claude Agent SDK, or similar frameworks. Design agent orchestration, agent-to-agent communication, tool integrations, and intelligent workflows. Own end-to-end AI solution architecture, from design and development to production deployment. Design data pipelines and integrate enterprise data sources using Azure, Databricks, Snowflake, and related technologies. Ensure data quality, accessibility, security, governance, and reliability for AI applications. Lead and mentor engineers while remaining actively involved in hands-on development. Collaborate with enterprise architects, advisory teams, and client stakeholders. Required Skills Strong hands-on experience in Agentic AI, Multi-Agent Systems, and RAG architecture. Expert-level Python programming and AI application development. Experience building AI agents and integrating existing LLMs such as Claude, OpenAI, or Azure OpenAI. Strong understanding of enterprise architecture, data engineering, ETL/ELT pipelines, and cloud platforms. Experience with agent frameworks, vector databases, API integrations, and AI orchestration. Strong technical leadership, problem-solving, and communication skills. Experience delivering enterprise AI solutions in a consulting or services environment is preferred. Key Expectation: We need a hands-on AI Architect who has personally designed, coded, and deployed AI agents and multi-agent solutions—not someone with only theoretical knowledge or high-level architecture experience. Building new LLMs from scratch is not required.