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 setting.





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