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

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.