Dell

1 day ago

Sr. Consultant Machine Learning & Knowledge Graph Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Lead the architecture, development, and deployment of enterprise-scale machine learning and knowledge graph solutions across Dell’s global ecosystem. Drive MLOps standards and build production-grade ML services, including graph-backed RAG and agentic AI query pipelines. Design ontology and semantic layers (OWL/RDF/RDFS) to unify data assets, operationalize graph algorithms, and deliver real-time ingestion with streaming and CDC. Provide technical leadership, mentorship, and executive recommendations on emerging graph technologies.

Location: United States

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Design, build, and scale enterprise knowledge graph platforms using Neo4j and/or Stardog, including graph-native data models for entity resolution, relationship discovery, and semantic reasoning.
•Define and govern enterprise ontologies and semantic layers (OWL 2) with consistent, reusable schemas that support inferencing and power agentic AI.
•Architect graph-backed agentic AI infrastructure such as RAG systems and dynamic prompt-to-graph query pipelines, and design virtualized/federated graph layers for real-time querying.
•Operationalize graph data science (e.g., community detection, centrality analysis, embeddings, link prediction) and build real-time graph ingestion pipelines using streaming and CDC.
•Establish graph governance (validation constraints, RBAC security, data lineage, ontology versioning), collaborate cross-functionally, and provide technical mentorship and technology recommendations.

Pay and Benefits

Salary: USD 235,450 - 304,700

Key Requirements

•Extensive experience designing and operating production-grade knowledge graph systems using Neo4j and/or Stardog, including ontology and semantic modeling (OWL/RDF/RDFS/SKOS) and large-scale schema evolution.
•Hands-on ability to build graph-backed environments for autonomous AI agents, including RAG, tool-calling orchestration, and dynamic SPARQL/Cypher generation from natural language.
•Expert command of distributed data systems and scale using PySpark, Kafka, data lakehouses (Apache Iceberg, Delta Lake), and enterprise orchestration (Airflow), with fluency in Python, SQL, Cypher, and SPARQL plus strong engineering practices (CI/CD, testing, version control, containerization).
•Experience operationalizing graph data science and analytics such as PageRank, Louvain, Label Propagation, and node embeddings (e.g., Node2Vec, FastRP) for downstream ML/AI pipelines.
•12+ years of progressive experience in data engineering/graph architecture/cloud-native platform delivery, including at least 4+ years focused on knowledge graph or semantic technology at enterprise scale.

Experience:12+ yearsData engineeringKnowledge graphsSemantic technologyDistributed systemsMLOps

Education:PhD / Doctorate in Technology, Computer Science, Machine Learning

Skills:CommunicationMentorshipStakeholder managementAdvisoryLeadership

Tech Stack:Neo4jStardogCypherGDSAPOCAuraDBCausal ClusteringSPARQLOWL 2Virtual GraphsSHACLSHEXRDFRDFSSKOSRAGRetrieval-Augmented GenerationVector-graph hybrid searchGraph Neural NetworksLLM-to-graph interfaces

Company Brief

Dell

Designs, manufactures and sells computers, servers, storage, networking equipment, and related IT solutions and services for consumers, small businesses, and enterprises worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Round Rock, United States

Founded: 1984

Glassdoor: 3.8

Website LinkedIn Glassdoor

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Dell

1 day ago

Sr. Consultant Machine Learning & Knowledge Graph Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: United States

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Design, build, and scale enterprise knowledge graph platforms using Neo4j and/or Stardog, including graph-native data models for entity resolution, relationship discovery, and semantic reasoning.
•Define and govern enterprise ontologies and semantic layers (OWL 2) with consistent, reusable schemas that support inferencing and power agentic AI.
•Architect graph-backed agentic AI infrastructure such as RAG systems and dynamic prompt-to-graph query pipelines, and design virtualized/federated graph layers for real-time querying.
•Operationalize graph data science (e.g., community detection, centrality analysis, embeddings, link prediction) and build real-time graph ingestion pipelines using streaming and CDC.
•Establish graph governance (validation constraints, RBAC security, data lineage, ontology versioning), collaborate cross-functionally, and provide technical mentorship and technology recommendations.

Pay and Benefits

Salary: USD 235,450 - 304,700

Key Requirements

•Extensive experience designing and operating production-grade knowledge graph systems using Neo4j and/or Stardog, including ontology and semantic modeling (OWL/RDF/RDFS/SKOS) and large-scale schema evolution.
•Hands-on ability to build graph-backed environments for autonomous AI agents, including RAG, tool-calling orchestration, and dynamic SPARQL/Cypher generation from natural language.
•Expert command of distributed data systems and scale using PySpark, Kafka, data lakehouses (Apache Iceberg, Delta Lake), and enterprise orchestration (Airflow), with fluency in Python, SQL, Cypher, and SPARQL plus strong engineering practices (CI/CD, testing, version control, containerization).
•Experience operationalizing graph data science and analytics such as PageRank, Louvain, Label Propagation, and node embeddings (e.g., Node2Vec, FastRP) for downstream ML/AI pipelines.
•12+ years of progressive experience in data engineering/graph architecture/cloud-native platform delivery, including at least 4+ years focused on knowledge graph or semantic technology at enterprise scale.

Experience:12+ yearsData engineeringKnowledge graphsSemantic technologyDistributed systemsMLOps

Education:PhD / Doctorate in Technology, Computer Science, Machine Learning

Skills:CommunicationMentorshipStakeholder managementAdvisoryLeadership

Company Brief

Dell

Designs, manufactures and sells computers, servers, storage, networking equipment, and related IT solutions and services for consumers, small businesses, and enterprises worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Round Rock, United States

Founded: 1984

Glassdoor: 3.8

Website LinkedIn Glassdoor