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
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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 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. 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.
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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.
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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.
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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
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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.
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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.
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