Senior Consultant Machine Learning & Knowledge Graph Engineer

Dell Technologies

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Round Rock, TX
Salary
$235,450–$304,700 / yr
Posted
2 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $229k
This role $270k
$170k $319k
below market most similar roles pay here above market

This role pays more than 88% of similar roles. Most pay $202,800–$254,750 — the blue band above. At the midpoint, this role pays about $270k versus about $229k for comparable roles.

Based on 240 similar postings.

Employer

About Dell Technologies

Dell Technologies is a US-based technology company that designs and sells PCs, servers, storage and networking hardware, along with related software and IT services. Its product lines include the XPS, Latitude and Alienware PCs and the PowerEdge server and PowerStore storage families.

Dell Technologies currently has 103 open roles on FindRole.

Listed pay typically runs $179,000–$240,790 across 92 roles with salary data.

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At a glance

TL;DR · Senior Consultant Machine Learning & Knowledge Graph Engineer

The Sr. Consultant Machine Learning & Knowledge Graph Engineer joins the Data Science team to lead the architecture, development, and deployment of enterprise-scale ML solutions. This role involves building production-grade Knowledge Graph platforms, designing semantic data layers for Agentic AI, and defining enterprise ontologies. Key responsibilities include architecting graph-backed Retrieval-Augmented Generation systems, managing data virtualization across SQL and NoSQL sources, and operationalizing advanced graph algorithms like community detection and node embeddings. The candidate will utilize technologies including Neo4j, Stardog, PySpark, Kafka, and Apache Iceberg while employing Python, SQL, Cypher, and SPARQL. The work focuses on solving complex business problems by creating a unified, machine-interpretable view of data assets through graph-native data models, semantic reasoning, and high-throughput ingestion pipelines to enable deterministic AI decision-making.

What you'll do

  • Design and scale enterprise Knowledge Graph platforms using Neo4j or Stardog to enable entity resolution and semantic reasoning.
  • Define and govern enterprise ontologies, taxonomies, and semantic schemas to provide a unified view of data assets.
  • Architect graph-backed Retrieval-Augmented Generation (RAG) systems and query pipelines to fuel autonomous AI agents.
  • Lead the design of virtualized graph layers to enable real-time querying across SQL, NoSQL, and streaming data sources.
  • Operationalize advanced graph algorithms like community detection and node embeddings to extract actionable intelligence.
  • Design high-throughput graph ingestion pipelines using Kafka, Spark Structured Streaming, and graph-native CDC mechanisms.
  • Establish graph data governance frameworks including SHACL validation, RBAC security models, and ontology versioning.
  • Evaluate emerging graph technologies and provide executive-level recommendations on adoption and innovation.

What we're looking for

  • 12+ years of progressive experience in data engineering, graph architecture, and cloud-native platform delivery.
  • 4+ years of experience focused specifically on Knowledge Graph or semantic technology initiatives at enterprise scale.
  • Extensive experience designing and operating production-grade graph systems using Neo4j or Stardog.
  • Proven expertise in enterprise ontology engineering including OWL 2 profiles, RDF/RDFS, SKOS taxonomies, and property graph modeling.
  • Deep practical understanding of building graph-backed data environments for autonomous AI agents and RAG systems.
  • Expert-level command over PySpark, Kafka, data lakehouses (Apache Iceberg, Delta Lake), and Airflow.
  • Advanced fluency in Python, SQL, Cypher, and SPARQL with strong software engineering practices like CI/CD and containerization.
  • PhD or Master's degree in Technology, Computer Science, Machine Learning, or an equivalent quantitative field (preferred).

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