AI Agent Engineer (RAG + Multi-Agent Systems)

We are looking for an AI engineer to build an internal AI-powered strategy assistant for marketing company. It must combine our internal resources with publicly available internet data to help our team work faster and produce higher-quality strategies. The AI should be able to: -Search and reason over our historical campaign data (RAG). -Analyze a brand's website and social media presence to identify tone of voice, visual style, content pillars, posting cadence, and previous influencer collaborations. -Generate campaign concepts, creative ideas, and influencer recommendations based on our historical knowledge. -Research competitors and identify seasonal marketing patterns. -Monitor social trends and surface emerging opportunities relevant to brands and creators. -Assist in creating campaign proposals and strategic briefs. Preferred Technical Experience: -Python -LLMs (OpenAI, Anthropic, Gemini) -RAG architecture (LangGraph, LangChain, or LlamaIndex) -Vector databases (Pinecone, Qdrant, Weaviate, or pgvector) -AI agent frameworks -Web crawling and structured data extraction -API integrations -Prompt engineering -Experience building production AI applications Deliverables: -AI architecture design -Working multi-agent system -RAG pipeline connected to our campaign library -Brand research and analysis agent -Creative strategy and ideation agent -Documentation and deployment guidance No need of cover letter. Please include examples of similar AI agents, RAG systems, or knowledge assistants you've built, along with the technologies you used and your role in the project.