Experience: 5+ Years

2.1 In-Scope Activities

- Knowledge Graph Architecture, Ontology, and Semantic Modeling

He/she will deliver technical capabilities required to design and build a scalable, semantically enriched Knowledge Graph, including:

- Ontology Design & Development
- Creation of formal ontology models aligned with business use cases.
- Use of industry-standard semantic frameworks, including:
- RDF (Resource Description Framework)
- OWL (Web Ontology Language)
- SKOS taxonomy models
- Definition of classes, properties, relationships, taxonomies, constraints, and semantic rules.

- Ontology Deployment & Governance Framework
- Versioned deployment process for ontology schema changes.
- Ontology promotion workflow integrating Our governance gates.
- Semantic modeling validation rules and automated consistency checks.

- Knowledge Graph Data Ingestion Pipelines
- Development of ingestion pipelines for structured/unstructured data.
- Alignment to ontology models and semantic rules.
- Graph model design using Neo4j’s property graph model.
- ETL/ELT transformations to map enterprise data to ontology and graph structures.

- CI/CD for Knowledge Graph

- CI/CD for Ontology Change Management
- Git-based versioning of ontology artifacts.
- Automated unit/integration validation of ontology changes.
- Build and release pipelines for ontology deployment across environments.
- Approval workflows for schema evolution and taxonomy updates.

- CI/CD for Data Change Management
- Automated ingestion pipeline deployments.
- Data versioning and rollback capabilities.
- Automated data quality checks, lineage tracking, and deduplication governance.
- GitHub-based source code management for all pipeline components.

- Neo4j AuraDB Integration and Platform Engineering

- Integration of Neo4j AuraDB SaaS
- Configuration of AuraDB instances.
- Connectivity setup with Our’s Brownfield Azure workplace.




- Role mapping, security integration, and policy alignment.

- Performance Monitoring
- Set up dashboards, alerts, and health checks.
- Baseline performance metrics (throughput, latency, capacity, query bottlenecks).
- SLA monitoring aligned with Neo4j-provided observability tools.
- Collaboration with Data Services Team for long-term operations.

- Integration of Neo4j-Generated ML Features

- Graph Data Science (GDS) Feature Outputs
- Implementation of GDS pipelines (node embeddings, graph metrics, community detection, etc.).
- Delivery of ML-ready feature sets for downstream modeling.

- Integration with Our ML Data Pipelines
- Seamless export of graph features into existing predictive AI workflows.
- Automated refresh of features as KG data changes.
- Integration patterns for Data Science and MLOps pipelines.

- Project Resourcing and Approval Requirements

Candidates must have verifiable:

- - Neo4j AuraDB experience
- Knowledge Graph/Ontology engineering background
- Graph Data Science/GDS experience
- Prior delivery of enterprise KG solutions

2.2 Out-of-Scope Activities

- Terraform or Infrastructure-as-Code Provisioning

- Neo4j AuraDB is a fully managed SaaS; infrastructure provisioning is performed by the vendor.
- Terraform or any IaC automation for provisioning Neo4j infrastructure is explicitly out of scope.
- Our will handle connectivity within the Brownfield environment but Terraform modules for Neo4j infrastructure will not be developed.

- General Azure Infrastructure Build





- No Azure resource creation unless explicitly required for pipeline or integration tasks.
- No management of Our enterprise infra services beyond required connectivity.

- Deliverables

3.1 Documentation & Architecture

- Knowledge Graph Reference Architecture
- Ontology Design Document (RDF/OWL Models, Taxonomies)
- Semantic Layer Integration Guide
- Data Mapping Specifications
- CI/CD Pipelines Documentation
- Neo4j Integration & Monitoring Playbook
- ML Feature Integration Patterns Document

3.2 Technical Assets

- Ontologies, semantic models, taxonomies
- Knowledge graph ingestion pipelines
- Ontology CI/CD pipelines
- Data ingestion CI/CD pipelines
- Neo4j configurations, dashboards, alerts (non-infrastructure)

3.3 Operational Deliverables

- Performance baseline and capacity assessments
- Semantic governance model and approval workflow
- Runbooks and support procedures

- Roles & Responsibilities

- Deliver all in-scope architecture, pipelines, ontology development, CI/CD, and integrations.
- Provide qualified resources approved by Our prior to onboarding.

- Provide SMEs for data and business modeling.
- Approve all candidate profiles.
- Provide required access to Azure, GitHub, and enterprise systems.
- Provide XPIQ data infrastructure and warehouse foundation prior to project start.

- Project Governance

- Weekly status calls
- Monthly executive updates
- Deliverable-based acceptance
- Stage-gated progression for ontology and KG development
- Our retains final approval on all architectural decisions

- Acceptance Criteria

- All deliverables completed per requirements
- Ontology models validated and deployed through CI/CD
- Ingestion pipelines operating with QA and lineage
- ML feature generation integrated with DS pipelines
- Performance dashboards operational and baselined

📌 Graph Data Engineer (India)
🏢 RDAlabs
📍 India