AI Engineer – Agentic AI & GraphRAG

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.