Graph AI Platforms | Onsite - (Dallas, Charlotte, New York)

Photon•United States,

2d•$52,000 - $182,000•Onsite

About The Position

We are seeking an experienced Graph AI Platforms professional to develop and enhance enterprise Graph AI platform capabilities, reusable graph services, and self-service graph analytics tools. This role involves designing and building Knowledge Graph solutions, Ontology frameworks, semantic models, and graph-powered applications. You will develop scalable graph APIs, microservices, and platform components, build graph intelligence frameworks, and develop Graph Neural Network (GNN) solutions and graph-based machine learning models. Additionally, you will build graph-enabled retrieval and inferencing services supporting Generative AI, GraphRAG, semantic search, and graph knowledge extraction, and implement data processing pipelines leveraging graph databases, distributed processing frameworks, and event-driven architectures. Collaboration with architects, data scientists, AI engineers, ontology engineers, and business stakeholders is key, as is participation in design discussions, code reviews, and Agile ceremonies. Ensuring solutions meet enterprise standards for security, scalability, governance, resiliency, and operational excellence is crucial. Support for platform observability, graph performance optimization, monitoring, and capacity management is also required. Continuous evaluation of emerging graph technologies and innovations is expected.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Mathematics, or a related technical discipline.
6+ years of software engineering experience with strong expertise in Python-based application development.
Hands-on experience with enterprise graph database technologies including Neo4j and/or TigerGraph.
Strong experience designing, developing, and managing large-scale Knowledge Graph and graph data platforms.
Experience developing Graph Analytics solutions utilizing graph algorithms for relationship intelligence, path analysis, community detection, centrality analysis, and pattern discovery.
Hands-on experience implementing and operationalizing Graph and graph-based machine learning models.
Expertise with GSQL, graph query languages, and graph data modeling techniques.
Strong Python programming skills with experience building production-grade applications, graph services, reusable libraries, and automated workflows.
Experience with model deployment, inference services, and graph-based AI/ML lifecycle management.
Experience building scalable REST APIs and microservices supporting graph workloads and AI-enabled applications.
Experience with Linux/Unix environments, Shell scripting, operational automation, and batch processing frameworks.
Experience managing enterprise schedulers including Autosys, Cron, or equivalent orchestration platforms.
Strong understanding of ontology concepts, semantic modeling, metadata management, knowledge representation, and graph-based knowledge systems.
Understanding of tokenomics, LLM integration patterns, graph-enhanced retrieval architectures, and AI inferencing techniques.
Experience working within large-scale engineering organizations utilizing Git-based development, CI/CD pipelines, automated testing, and Agile delivery methodologies.
Familiarity with distributed computing environments, containerized workloads, and cloud-native engineering practices.

Nice To Haves

Experience with Deep Graph Library (DGL), PyTorch Geometric, or other graph machine learning frameworks.
Experience building GraphRAG, graph-powered retrieval, and Graph AI solutions supporting Generative AI use cases.
Knowledge of ontology management frameworks, semantic web technologies, RDF, OWL, and enterprise knowledge representation patterns.
Experience implementing graph embeddings, knowledge embeddings, graph feature engineering, and graph representation learning techniques.
Familiarity with enterprise AI governance, model governance, responsible AI, metadata management, and data quality practices.
Knowledge of Data Governance, including Data Catalog, Data Lineage, and Data Quality frameworks.
Exposure to enterprise-scale Graph AI platforms supporting Cybersecurity, Fraud Detection, Risk Analytics, Customer Intelligence, and Knowledge Management use cases.
Experience integrating graph technologies with LLMs, vector databases, AI orchestration frameworks, and multimodal knowledge systems.
Familiarity with cloud-native graph deployments, Kubernetes, container platforms, and distributed computing environments.

Responsibilities

Develop and enhance enterprise Graph AI platform capabilities, reusable graph services, and self-service graph analytics tools.
Design and build Knowledge Graph solutions, Ontology frameworks, semantic models, and graph-powered applications supporting enterprise business use cases.
Develop scalable graph APIs, microservices, and platform components supporting graph ingestion, graph processing, graph analytics, and model inferencing.
Build and maintain graph intelligence frameworks supporting entity resolution, relationship discovery, graph prediction, link analysis, anomaly detection, and knowledge enrichment.
Develop Graph Neural Network (GNN) solutions and graph-based machine learning models for predictive analytics and intelligent decision-making.
Build graph-enabled retrieval and inferencing services supporting Generative AI, GraphRAG, semantic search, and graph knowledge extraction.
Implement data processing pipelines leveraging graph databases, distributed processing frameworks, and event-driven architectures.
Collaborate with architects, data scientists, AI engineers, ontology engineers, and business stakeholders to deliver enterprise graph capabilities.
Participate in design discussions, code reviews, sprint planning, story refinement, estimation activities, and technical governance reviews.
Ensure solutions meet enterprise standards for security, scalability, governance, resiliency, and operational excellence.
Support platform observability, graph performance optimization, monitoring, and capacity management initiatives.
Continuously evaluate emerging graph technologies, graph machine learning frameworks, and Graph AI innovations to enhance platform capabilities.