AI Architect - RAG & Agentic AI
Description
Hello,
Spiceorb is looking for AI Architect - RAG & Agentic AI
Role: AI Architect – Agentic AI & RAGLocation: RemoteDuration: 12+ Months Contract Job SummaryWe 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.
- 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.

