Description

You will lead the development, support, and implementation of enterprise-grade graph data models in Neo4j, ensuring scalability, consistency, and alignment with business domains such as customer 360, fraud detection, and supply chain.

This role is on-site.

Responsibilities

• Design and implement Neo4j architecture models, including clustering, indexing strategies, query optimization, caching, and high-availability deployments for large-scale workloads.
• Establish standards for Cypher queries, GraphQL integrations, APOC procedures, and API design to ensure maintainability and performance across engineering teams.
• Drive adoption of Graph Data Science (GDS) capabilities for recommendations, pathfinding, and community detection by collaborating with data science and AI engineering teams.
• Integrate Neo4j with cloud-native platforms, streaming systems like Kafka, vector databases, and enterprise data lakes to support hybrid architectures.
• Analyze user requirements, define technical project scope, and create technical designs for new systems or modifications to existing systems.

Required Skills

• 10+ years of overall experience in designing and developing Neo4j-based Knowledge Graph and graph data solutions using enterprise integration and distributed architectures.
• Hands-on experience with Neo4j, Cypher Query Language, APOC procedures, GraphQL, RDF/SPARQL concepts, and semantic data modeling.
• Strong experience in building and managing teams with expertise in Knowledge Graphs, including ontology modelling, metadata management, and graph traversal optimization.
• Hands-on experience integrating Neo4j with Microservices, Kafka, REST APIs, Spring Boot, OpenShift, Kubernetes, and cloud-native platforms.
• Experience implementing Graph Data Science (GDS) algorithms including pathfinding, similarity analysis, recommendation engines, and network analytics.
• Experience in building scalable and containerized graph applications using Docker, OpenShift, Kubernetes, Jenkins, GitHub, and CI/CD pipelines.
• Strong understanding of graph database performance tuning, indexing strategies, query optimization, and high-availability deployment architectures.

Preferred Skills

• Hands-on experience integrating Knowledge Graphs with AI/ML and Generative AI frameworks, including Retrieval-Augmented Generation (RAG), semantic search, and vector embeddings.

Key Skills

Education

Any Graduate