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 transparent 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.

📌 AI Engineer Agentic AI & GraphRAG (Hyderabad)
🏢 Important Group
📍 Hyderabad