AI Engineer – Agentic AI & GraphRAG

Agivant; Hyderabad; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Agivant Technologies India Private Limited is seeking an AI Engineer to work on GraphRAG systems and MCP tooling, spanning AI/LLM integration, graph query pipelines, and developer tooling. You’ll influence the direction of agentic-AI components and write code alongside a broader engineering team.

This role requires hands-on experience with Python, TigerGraph, and modern AI SDKs, and entail building reusable modules for cognitive agents within a distributed platform.

Qualifications

• 3-6 years of hands-on software engineering experience, incl. exposure to LLM orchestration, agent systems, or AI SDKs.

Responsibilities

• Contribute to GraphRAG systems and MCP framework components, with guidance from senior engineers.

Skills

Python

LLM orchestration

Agent systems

AI SDKs

LangChain

LangGraph

Vector indexing

GSQL

Tools

TigerGraph

Docker

Kubernetes

FastAPI

RESTPP

GSQL

Job description

Agivant Technologies India Private Limited | Full time

Agivant is a new-age AI-First Digital and Cloud Engineering services company that drives Agility and Relevance for our client’s success.

Powered by cutting-edge technology solutions that enable new business models and revenue streams, we help our clients achieve their trajectory of growth.

Agility is a core muscle, an integral part of the fabric of a modern enterprise. To succeed in an ever-changing business environment, every modern organization needs to adapt and renew itself quickly. We help foster a more agile approach to business to reconfigure strategy, structure, and processes to achieve more growth and drive greater efficiencies.

Relevance is timeless and is the only way to survive and thrive.

The quest for relevance defines the exponential acceleration of humanity. This has presented us with a slew of opportunities, but also many unprecedented challenges. With technology-led innovation, we help our customers harness these opportunities and address myriad challenges.

Job Description

AI Engineer – Agentic AI & GraphRAG Development

We are looking for a talented and self-driven AI Engineer towork on our GraphRAG (Graph Retrieval-Augmented Generation) systems andcontribute to the evolution of Graph's MCP (Model Context Protocol) toolingframework. This role spans AI/LLM integration, graph query pipelines, anddeveloper tooling — helping build a platform that blends graph intelligencewith generative AI.

This is a role for someone who enjoys solving open-endedproblems. You'll work from clear objectives rather than fully scoped tickets,contribute to the direction of GraphRAG and agentic-AI components, and writethe code to bring them to life alongside a broader engineering team.

Responsibilities

• Contributeto GraphRAG systems and MCP framework components, working throughambiguous technical problems with guidance from senior engineers whereneeded
  
• Designand build MCP tools and components, including orchestration logic,agentic-AI workflows, LLM interface layers, and graph-native operators
  
• Buildintegration code between TigerGraph's GSQL, vector indexing systems, andexternal LLMs (e.g., OpenAI, Gemini, LLaMA)
  
• Developreusable modules, prompts, and components for cognitive agents (e.g.,GraphRAG agents, schema routers, grounded QA evaluators) with attention todeveloper experience
  
• Collaboratewith TigerGraph's platform, AI research, and product teams to help shapethe MCP engineering roadmap
  
• Writetest suites and benchmark GraphRAG system performance for hallucination,groundedness, latency, and answer usability
  
• Contributeto internal documentation and SDKs to support MCP developer usability
  

Required:

• Experience: 3-6 years of hands-on software engineering experience, including exposureto LLM orchestration, agent systems, or AI SDKs
  
• OwnershipMindset: Comfortable working through loosely defined problems andproposing solutions, with support from senior team members as needed
  
• Strongprogramming skills in Python
  
• Workingexperience with TigerGraph (GSQL queries, RESTPP, schema modeling), orstrong experience with another graph database and willingness to ramp up
  
• Familiaritywith Graph-based retrieval-augmented generation (GraphRAG) architecturesand their application in real-world AI systems
  
• Experienceusing frameworks like LangChain, LangGraph, or similar agent-based LLMtools and prompt templating
  
• Understandingof vector indexing and similarity search; familiarity with vector stores(e.g., FAISS, Milvus)
  
• Abilityto build usable internal tools for developers or data scientists
  

Preferred:

• Priorexperience contributing to tools, platforms, or APIs used by other AIengineers or ML practitioners
  
• Backgroundin knowledge graphs, graph neural networks, or knowledge-based QA systems
  
• Familiaritywith Docker/Kubernetes, FastAPI, and distributed compute systems
  
• Contributionsto 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 otherdevelopers will interact with your code.
• Strong programming skills inPython; deep hands-on experience building LLM orchestration tools, agentsystems, or AI SDKs.
• Hands‑onexperience with TigerGraph (GSQL queries, RESTPP, schema modeling).
• Familiaritywith Graph-based retrieval‑augmented generation (GraphRAG) architectures andtheir 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.
• Understandingof 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.
• Familiaritywith Docker/Kubernetes, FastAPI, and distributed compute systems.
• Contributions to open-source projects in the graph, ML, or LLM domains.