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 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.

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.