Application Developer - Graph Database AI Developer- 9214

Job description

Job Description Application Developer - Graph Database AI Developer- 9214 Job Location: Bangalore, Chennai, Hyderabad, Noida, Pune Location Flexibility: Multiple Locations in Country Req Id: 9214 Posting Start Date: 6/24/26 At Fujitsu, our purpose is to make the world more sustainable by building trust in society through innovation. Founded in Japan in 1935, Fujitsu has been a pioneer in technology and innovation for decades. Today, as a world-leading digital transformation partner, we are committed to transforming business and society in the digital age. With approximately 130,000 employees across over 50 countries, Fujitsu offers a broad range of products, services, and solutions. We collaborate with our customers to co-create solutions that drive enterprise-wide digitalization while actively working to address social issues and contribute to the United Nations Sustainable Development Goals (SDGs). **Job Title: Application Developer - Graph Database AI Developer- 9214** **Shift: 2:00PM-11:00PM** **Locations: Pune, Bangalore, Chennai, Hyderabd, Noida** **Experience: 3-6 Years** **Job Description – Graph Database AI Developer** **Neo4j | Knowledge Graph | GraphRAG | Python | LangChain** **Experience:** - **3-6 years** of overall software development experience. - Minimum **2+ years** of hands-on Neo4j implementation experience. - Practical experience in AI, GenAI, knowledge graphs, or GraphRAG solutions is preferred. **Role Summary:** We are looking for an experienced **Graph Database AI Developer** . The candidate will design and develop enterprise knowledge graph and GraphRAG solutions using **Neo4j** . The role will include graph data modelling, Cypher query development, data ingestion, API development, and LLM integration. The candidate will also build scalable AI-powered applications using **Python, LangChain, Neo4j, REST APIs, and GraphQL** . **Primary Skills:** - Neo4j - Cypher Query Language - Graph data modelling - Knowledge Graphs - GraphRAG architecture - Python - LangChain - REST APIs - GraphQL - Data ingestion and transformation - LLM integration Query optimisation and performance tuning **Key Responsibilities:** **1. Graph Database Design and Development** - Design and develop graph database solutions using **Neo4j** . - Create scalable graph data models for enterprise use cases. - Define nodes, relationships, properties, labels, and graph patterns. - Design constraints and indexes for data quality and performance. - Maintain clear graph schema and modelling standards. - Select the right graph modelling approach based on business needs. **2. Cypher Query Development** - Develop complex and reusable **Cypher queries** . - Build queries for graph traversal, pattern matching, and relationship analysis. - Develop Cypher procedures for business and AI use cases. - Review and optimise existing queries. - Analyse query execution plans and identify performance issues. - Improve query response time for large graph datasets. **3. Knowledge Graph Development** - Design and implement enterprise knowledge graph solutions. - Convert business concepts into entities and relationships. - Build domain-specific ontologies and taxonomies where required. - Connect data from multiple business systems. - Support entity resolution and relationship discovery. - Ensure knowledge graph data is accurate, traceable, and easy to use. **4. GraphRAG Solution Development** - Design and implement **GraphRAG architectures** . - Integrate Neo4j knowledge graphs with LLM-based applications. - Retrieve relevant entities, relationships, and graph paths for user queries. - Combine graph context with unstructured document content. - Improve answer relevance using graph-based retrieval. - Develop prompts using retrieved graph information. - Implement source references and explainable responses. - Reduce unsupported or incorrect LLM responses through proper grounding. **5. LLM and AI Framework Integration** - Integrate Neo4j with approved LLM platforms. - Develop AI workflows using **LangChain** . - Build graph-based tools and agents for enterprise use cases. - Integrate embedding models and semantic search where required. - Develop prompt templates and structured output handling. - Implement guardrails for secure and responsible AI usage. - Support evaluation of AI response quality and retrieval accuracy. **6. Data Ingestion and Graph Transformation** - Build data ingestion pipelines for structured and unstructured data. - Ingest data from: - relational databases - APIs - JSON and CSV files - documents - cloud storage - enterprise applications - Transform source data into graph entities and relationships. - Implement full-load and incremental-load approaches. - Handle duplicate entities and inconsistent data. - Build data validation, reconciliation, and error-handling steps. - Maintain data lineage and ingestion logs. **7. Python Application Development** - Develop scalable backend services using **Python** . - Write clean, reusable, and maintainable code. - Use modern Python frameworks such as **FastAPI or Flask** . - Develop modules for ingestion, retrieval, graph queries, and AI processing. - Implement proper logging, configuration, and exception handling. - Write unit tests and integration tests. - Troubleshoot application and performance issues. **8. API and Microservices Development** - Develop APIs to expose graph intelligence capabilities. - Build **REST and GraphQL interfaces** . - Develop services for graph search, recommendations, and relationship analysis. - Implement secure API authentication and authorisation. - Integrate graph services with enterprise applications. - Maintain API documentation and usage examples. - Ensure APIs are scalable and easy to monitor. **9. Neo4j Administration and Monitoring** - Support Neo4j installation, configuration, and environment setup. - Monitor database health, storage, memory, and query performance. - Manage da