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

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Neo4j Administration and Monitoring
• Support Neo4j installation, configuration, and environment setup.
• Monitor database health, storage, memory, and query performance.
• Manage database users, roles, and access permissions.
• Support backup, restore, and disaster recovery activities.
• Monitor slow queries and resource usage.
• Support Neo4j version upgrades and patching.
• Work with infrastructure teams for high availability and scaling.
• Performance Optimisation
• Optimise graph models and Cypher queries.
• Create suitable indexes and constraints.
• Improve ingestion performance for large datasets.
• Analyse memory usage and transaction performance.
• Tune Neo4j configuration based on workload.
• Perform load and performance testing.
• Ensure the application meets agreed response-time requirements.
• Security and Governance
• Implement role-based access controls.
• Follow least-privilege access principles.
• Protect sensitive business and personal data.
• Support encryption and secure credential management.
• Ensure graph and AI solutions follow client security standards.
• Maintain audit logs and data access records.
• Support data retention and compliance requirements.
• Testing and Quality Assurance
• Prepare unit, integration, and functional test cases.
• Validate graph relationships and query results.
• Test ingestion pipelines and AI workflows.
• Validate GraphRAG retrieval and answer quality.
• Perform regression testing after changes.
• Investigate defects and complete root cause analysis.
• Implement preventive actions to avoid repeated issues.
• Documentation and Delivery
• Prepare: + requirement documents + graph data models + solution design documents + architecture diagrams + API documents + deployment guides + operational runbooks
• Provide effort estimates and delivery plans.
• Share regular progress updates.
• Raise risks and dependencies early.
• Support deployment and post-production activities.
• Team Collaboration
• Work with Data Engineers, AI Engineers, Data Scientists, and Architects.
• Work closely with business and domain teams.
• Participate in requirement and design discussions.
• Review code, graph models, and technical designs.
• Guide junior developers where required.
• Support technical interviews and skill assessments.

Mandatory Skills Neo4j and Graph Database

• Strong hands-on experience with Neo4j.
• Advanced knowledge of Cypher Query Language.
• Strong graph data modelling and schema design skills.
• Experience with nodes, relationships, labels, properties, and graph patterns.
• Experience with indexes, constraints, and query optimisation.
• Knowledge of Neo4j administration and monitoring.
• Strong understanding of graph database use cases and limitations.

AI and GraphRAG

• Understanding of knowledge graph and GraphRAG architectures.
• Experience integrating graph databases with LLM applications.
• Knowledge of retrieval, grounding, prompt construction, and answer generation.
• Understanding of embeddings and semantic retrieval.
• Ability to validate AI responses and retrieval quality.

Application Development

• Advanced hands-on experience in Python.
• Strong experience with LangChain.
• Experience in backend application development.
• Experience developing REST APIs.
• Good understanding of GraphQL.
• Knowledge of microservices architecture.
• Strong debugging and problem-solving skills.

Data Engineering

• Experience in data ingestion and transformation.
• Good understanding of structured and unstructured data.
• Knowledge of data validation and reconciliation.
• Familiarity with relational databases and SQL.
• Experience handling large and complex datasets.

Good-to-Have Skills

• Neo4j Graph Data Science Library.
• Neo4j APOC procedures.
• Neo4j Aura or managed Neo4j cloud services.
• Experience with vector search in Neo4j.
• Knowledge of ontology and taxonomy design.
• Entity resolution and record-linking experience.
• Experience with LlamaIndex or similar AI frameworks.
• Knowledge of OpenAI, Azure OpenAI, Anthropic, Gemini, or open-source LLMs.
• Experience with RAG evaluation frameworks.
• FastAPI or Flask development experience.
• Docker and container-based deployment.
• Kubernetes exposure.
• AWS, Azure, or GCP experience.
• Git and CI/CD pipeline experience.
• Monitoring and observability tools.
• Experience with Kafka or other event-driven platforms.
• Familiarity with RDF, OWL, SPARQL, or semantic web standards.

Tools And Technology Stack Graph Technologies

• Neo4j
• Cypher
• APOC
• Neo4j Graph Data Science
• Neo4j Aura
• GraphQL

AI and GenAI

• LangChain
• LLM platforms
• Embedding models
• GraphRAG
• Vector search
• Prompt engineering
• AI evaluation tools

Development

• Python
• FastAPI or Flask
• REST APIs
• Git
• Docker
• CI/CD tools

Data Sources

• SQL databases
• APIs
• JSON and CSV
• Documents
• Cloud storage
• Enterprise data platforms

Qualification

• Bachelor’s or Master’s degree in: + Computer Science + Information Technology + Artificial Intelligence + Data Science + Information Systems + or a related discipline A strong practical background can also be considered.

Preferred Certifications

• Neo4j Certified Professional.
• Neo4j Graph Data Science certification.
• Cloud or AI-related certification.
• Python or data engineering certification. Certifications are preferred but not mandatory.

Soft Skills

• Strong analytical and problem-solving skills.
• Clear verbal and written communication.
• Good client and stakeholder interaction.
• Strong ownership and accountability.
• Ability to explain complex graph concepts in simple language.
• Good documentation habits.
• Ability to work independently and within a team.
• Research-oriented and willing to learn new AI technologies.

Preferred Candidate Profile The preferred candidate should have:

• Strong real-project experience with Neo4j.
• Hands-on delivery experience with knowledge graphs.
• Good understanding of GraphRAG and LLM integration.
• Ability to own work from requirement gathering to production support.
• Strong Python and backend development skills.
• Experience building scalable and secure enterprise applications.
• Ability to balance solution accuracy, performance, security, and delivery timelines. Relocation Supported: No Visa Sponsorship Approved: No At Fujitsu, we are committed to an inclusive recruitment process that values the diverse backgrounds and experiences of all applicants. We believe that hiring people from a wide variety of backgrounds makes us stronger, not because it's the right thing to do, but because it allows us to draw on a wider range of perspectives and life experiences.