Description

We are
looking for a talented, highly autonomous, and self -driven AI Engineer to take
hands -on ownership of our GraphRAG (Graph Retrieval -Augmented Generation)
systems and contribute directly to the evolution of Graph's MCP (Model
Context Protocol) tooling framework. This role spans deep AI/LLM integration,
graph query pipelines, and developer tooling—helping build a cutting -edge
platform that blends graph intelligence with generative AI.

This
is a role for a true builder who thrives in ambiguity. You won’t just be handed a neatly
packaged list of Jira tickets; you will be expected to explore the bleeding
edge of GraphRAG, identify what our framework needs next, and write the code to
make it happen. If you are deeply curious, thoughtful in your architectural
decisions, and motivated by independently solving complex engineering problems
from conceptualization to deployment, you will thrive here.

Responsibilities

●       Act as a self -directed engineer: Navigate ambiguous technical challenges, make
sound architectural trade -offs, and drive the execution of GraphRAG systems
using TigerGraph’s MCP framework without needing a step -by -step roadmap.

●       Proactively design and develop: Identify gaps and build essential MCP tools
and components from the ground up, including orchestration logic, agentic -AI
workflows, LLM interface layers, and graph -native operators.

●       Architect with foresight: Build elegant, schema -aware integration code between TigerGraph’s GSQL,
vector indexing systems, and external LLMs (e.g., OpenAI, Gemini, LLaMA),
anticipating future platform scale.

●       Build for developers: Thoughtfully engineer reusable modules, prompts, and components for
cognitive agents (e.g., GraphRAG agents, schema routers, grounded QA
evaluators) with a strong focus on developer experience and API design.

●       Collaborate as a technical partner: Work closely alongside TigerGraph’s platform,
AI research, and product teams to shape the MCP engineering roadmap, bringing
your own solutions to usability and scalability hurdles.

●       Ensure engineering excellence: Independently write test suites and benchmark
the performance of GraphRAG systems for hallucination, groundedness, latency,
and answer usefulness, holding your own code to the highest standard.

●       Empower the community: Write highly readable internal documentation and clear SDKs to improve
the developer usability of MCP.

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.