Principal AI Architect for Enterprise AI Platform

An enterprise AI platform is scaling quickly. It is running more customer pilots and taking on heavier platform demands, and it needs a senior architect to set its technical direction. You will own the architecture of its retrieval and knowledge layer: how enterprise data is structured, connected through knowledge graphs, and retrieved for LLMs and AI agents. You will also act as the senior technical voice for a growing engineering team. The work balances hands-on design with technical leadership. You'll fix current problems with performance, accuracy, and scalability, and you'll shape the approach for the customer-specific projects coming next.

What You'll Do

• Provide senior architectural leadership across the AI platform, with clear technical direction for Generative AI, retrieval, and data design.
• Design, evaluate, and improve RAG and GraphRAG architectures for enterprise AI applications.
• Build and optimize knowledge graph and retrieval architectures, including ontology and semantic data modeling.
• Diagnose and resolve challenges in retrieval accuracy, latency, scalability, and cost.
• Improve how enterprise data is structured, indexed, and retrieved by AI agents.
• Work hands-on with Neo4j, Weaviate, and LangChain, or with equivalent graph, vector, and orchestration tools.
• Support the development of AI agents and their integration with enterprise data and workflows.
• Define technical approaches and reference architectures for upcoming customer-specific pilots.
• Mentor junior AI engineers through design reviews, pairing, and technical guidance.
• Partner with engineering leadership and technical stakeholders so the architecture stays scalable and maintainable as the customer base grows.

What You Bring

• Senior- or principal-level experience in Generative AI and AI architecture.
• Strong hands-on experience designing and shipping RAG and/or GraphRAG systems in production.
• Deep understanding of knowledge graphs, ontologies, and semantic data modeling.
• Experience with Neo4j or a comparable graph database (e.g., Amazon Neptune, TigerGraph, Stardog).
• Experience with Weaviate or a comparable vector database (e.g., Pinecone, Qdrant, Milvus, pgvector).
• Strong experience with LangChain or a comparable LLM orchestration framework (e.g., LlamaIndex, Haystack, Semantic Kernel).
• A track record of designing production-grade AI systems, with a strong grasp of LLM-based retrieval and data architecture.
• Experience leading or mentoring engineers within a collaborative team.
• Excellent communication skills, including the ability to explain architectural trade-offs to technical and non-technical stakeholders.

Nice to Have

• Experience building enterprise AI platforms or agentic AI systems.
• Background in AI governance, security, or data access controls (e.g., permission-aware retrieval).
• Experience optimizing AI systems for accuracy, performance, and cost, including evaluation frameworks.
• Experience delivering customer-specific AI implementations or pilots.
• Experience in a fast-growing startup environment.
• Strong Python skills and familiarity with modern AI/ML development frameworks.