AI Engineer - AM

Knowledge Graph & Agentic GenAI Specialist

Role Overview

We are seeking a Knowledge Graph & Agentic GenAI Specialist with X+ years of experience to lead and deliver enterprise-scale GenAI and Knowledge Graph solutions.

This role sits within our Data & AI Consulting practice and focuses on building reasoningheavy, graphcentric GenAI systems for complex business domains. The ideal candidate brings deep expertise in Knowledge Graph engineering, combined with hands-on experience in Agentic AI and Graph RAG architectures, and can lead client engagements from ideation through production deployment.

Key Responsibilities

Knowledge Graph & Graph RAG

Design, build, and scale enterprise Knowledge Graphs, including schema, ontology, and relationship modeling for complex, highcardinality domains; operate and optimize Neo4j platforms at scale with strong proficiency in Cypher, performance tuning, and ingestion pipelines. Architect and implement Graph RAG and Agentic GenAI solutions, combining Knowledge Graphs with LLMs using frameworks such as LangGraph, enabling multistep reasoning, hybrid Graph + Vector retrieval, and endtoend GenAI architectures from ingestion through inference.

Delivery, Leadership & Consulting

Lead project delivery and ensure client business objectives are achieved endtoend. Contribute to solution ideation, technical design, and client workshops with stakeholders. Support pre-sales activities, including solution design, demos, and technical inputs for proposals. Build secure, scalable APIs and microservices, collaborating with data, ML, and product teams. Mentor, upskill, and provide technical guidance to team members; perform design and code reviews.

Required Skills & Experience

6+ years of hands-on experience in Knowledge Graphs, GenAI, or Agentic AI solutions. Strong hands-on experience with Neo4j (Enterprise preferred). Advanced proficiency in Cypher, including query optimization, indexing, and constraints. Proven experience managing large, complex, and evolving graph datasets, ideally at TB scale. Solid understanding of graph theory, traversal patterns, and graph algorithms. Hands-on experience with LangChain and LangGraph, especially for Graph RAG and agent orchestration. Strong understanding of RAG architectures, with emphasis on Graph RAG.

Experience integrating LLMs with structured data systems, embeddings, and vector databases. Strong Python development skills; hands-on experience with SQL and API development (REST/GraphQL/Cypher). Experience with at least one cloud platform: Azure, AWS, or GCP. Familiarity with Docker, Kubernetes, and CI/CD pipelines. Exposure to classical machine learning and data engineering is a plus.

Qualifications

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related fields.

Demonstrated experience leading end-to-end solution delivery from ideation to deployment in client-facing environments.