This role pays more than
83%
of similar roles. Most pay
$202,387–$254,750
— the shaded 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
73 open roles
on FindRole.
Listed pay typically runs
$164,025–$225,750
across 64 roles with salary data.
As a Sr. Consultant Machine Learning & Knowledge Graph Engineer, you will join a dynamic team to lead the architecture, development, and deployment of enterprise-scale machine learning solutions. You will build production-grade ML services, create enterprise-wide knowledge graph marketplaces, and design semantic data layers to power Agentic AI. Your daily work involves designing graph-native data models for entity resolution, governing ontologies using OWL 2, and architecting Retrieval-Augmented Generation systems with tool-calling interfaces. You will utilize technologies including Neo4j, Stardog, Cypher, SPARQL, Python, SQL, PySpark, Kafka, and Spark Structured Streaming. Additionally, you will operationalize graph algorithms like PageRank and node embeddings while managing data virtualization across SQL and NoSQL sources. This role solves complex problems in knowledge representation, ensuring consistent, machine-interpretable views of data assets through advanced graph-based infrastructure and robust governance frameworks.
Machine Learning
Natural Language Processing
PyTorch
TensorFlow
JAX
Python
Go
Java
C++
Computer Vision
Deep Learning
Content Moderation
Adversarial Training
Search Ranking
Information Retrieval
Multimodal Content Filtering
Machine Learning
Natural Language Processing
PyTorch
TensorFlow
JAX
Python
Go
Java
C++
Computer Vision
Deep Learning
Search Ranking
Retrieval Pipelines
Adversarial Training
Content Moderation
Red-teaming