Job DescriptionPrimary Skill: Graph AI Platforms
Secondary Skills: Graph Databases, Graph Analytics, Machine Learning
Tertiary Skills: Python Development, Platform Engineering, Automation
Minimum Experience: 8+ Years
Key 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.
Required Qualifications- 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.
Core Engineering Responsibilities- Develop code and automated tests to deliver stories and requirements meeting quality, compliance, and operational standards.
- Participate in application design leveraging graph architecture, semantic modeling, AI architecture, integration patterns, and enterprise platform standards.
- Collaborate in requirement analysis, story refinement, ontology design, data modeling, and solution architecture activities.
- Estimate and deliver assigned work within Agile development cycles.
- Build Graph AI applications, graph prediction engines, graph inferencing services, ontology-driven knowledge frameworks, and graph-enabled retrieval solutions.
- Develop graph ingestion, transformation, enrichment, and knowledge-building pipelines supporting enterprise graph ecosystems.
- Deliver secure, scalable, observable, and resilient graph solutions aligned with enterprise engineering standards.
- Troubleshoot, optimize, and maintain graph databases, graph analytics services, AI inferencing workloads, and platform components to ensure operational excellence.
- Support graph performance tuning, query optimization, graph model governance, and graph lifecycle management.
- Contribute to graph standards, reusable frameworks, reference architectures, and engineering best practices across the Graph AI platform.
Preferred Qualifications- 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.
Compensation, Benefits and DurationMinimum Compensation: USD 52,000
Maximum Compensation: USD 182,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post
