AI Engineer – Agentic AI & GraphRAG Development We are looking for a talented and self-driven AI Engineer to work on our GraphRAG (Graph Retrieval-Augmented Generation) systems and contribute to the evolution of Graph's MCP (Model Context Protocol) tooling framework. This role spans AI/LLM integration, graph query pipelines, and developer tooling — helping build a platform that blends graph intelligence with generative AI. This is a role for someone who enjoys solving open-ended problems. You'll work from clear objectives rather than fully scoped tickets, contribute to the direction of GraphRAG and agentic-AI components, and write the code to bring them to life alongside a broader engineering team. Responsibilities Contribute to GraphRAG systems and MCP framework components, working through ambiguous technical problems with guidance from senior engineers where needed Design and build MCP tools and components, including orchestration logic, agentic-AI workflows, LLM interface layers, and graph-native operators Build integration code between TigerGraph's GSQL, vector indexing systems, and external LLMs (e.g., OpenAI, Gemini, LLaMA) Develop reusable modules, prompts, and components for cognitive agents (e.g., GraphRAG agents, schema routers, grounded QA evaluators) with attention to developer experience Collaborate with TigerGraph's platform, AI research, and product teams to help shape the MCP engineering roadmap Write test suites and benchmark GraphRAG system performance for hallucination, groundedness, latency, and answer usefulness Contribute to internal documentation and SDKs to support MCP developer usability Required: Experience: 3-6 years of hands-on software engineering experience, including exposure to LLM orchestration, agent systems, or AI SDKs Ownership Mindset: Comfortable working through loosely defined problems and proposing solutions, with support from senior team members as needed Strong programming skills in Python Working experience with TigerGraph (GSQL queries, RESTPP, schema modeling), or strong experience with another graph database and willingness to ramp up Familiarity with Graph-based retrieval-augmented generation (GraphRAG) architectures and their application in real-world AI systems Experience using frameworks like LangChain, LangGraph, or similar agent-based LLM tools and prompt templating Understanding of vector indexing and similarity search; familiarity with vector stores (e.g., FAISS, Milvus) Ability to build usable internal tools for developers or data scientists Preferred: Prior experience contributing to tools, platforms, or APIs used by other AI engineers or ML practitioners Background in knowledge graphs, graph neural networks, or knowledge-based QA systems Familiarity with Docker/Kubernetes, FastAPI, and distributed compute systems Contributions to open-source projects in the graph, ML, or LLM domains Requirements Required: ● High Agency & Self-Drive: A proven track record of taking vague technical concepts, figuring out the optimal engineering path, and writing production-ready code without requiring heavy hand-holding or day-to-day micro-direction. ● Product-Minded Engineer: You don't just write scripts; you think deeply about the "why" behind the feature and care immensely about how other developers will interact with your code. ● Strong programming skills in Python; deep hands-on experience building LLM orchestration tools, agent systems, or AI SDKs. ● Hands-on experience with TigerGraph (GSQL queries, RESTPP, schema modeling). ● Familiarity with Graph-based retrieval-augmented generation (GraphRAG) architectures and their application in real-world AI systems. ● Experience using or actively contributing to frameworks like LangChain, LangGraph, or similar agent-based LLM tools and prompt templating. ● Understanding of vector indexing and similarity search; familiar with modern vector stores (e.g., FAISS, Milvus). ● Ability to design exceptionally usable internal tools for developers or data scientists. Preferred: ● Prior experience developing tools, platforms, or APIs used by other AI engineers or ML practitioners. ● Background in knowledge graphs, graph neural networks, or knowledge-based QA systems. ● Familiarity with Docker/Kubernetes, FastAPI, and distributed compute systems. ● Contributions to open-source projects in the graph, ML, or LLM domains.