Sr. Consultant Machine Learning & Knowledge Graph Engineer

Socket.dev; Town of Texas (WI); On-site; USD 140,000 - 230,000; Full time

Job summary

Dell Technologies seeks a Sr. Consultant Machine Learning & Knowledge Graph Engineer to lead architecture and deployment of enterprise-scale ML and knowledge-graph solutions. You will drive MLOps standards, production-grade graph services, and semantic data layers across business domains.

You will design and govern ontology, pursue scalable graph data strategies, and mentor a team while collaborating with product, platform, and engineering stakeholders to enable AI at scale.

Qualifications

• Extensive hands-on experience designing and operating production-grade graph systems.
• Proven enterprise ontology engineering and semantic modeling skills.
• Deep knowledge of graph-backed AI and RAG pipelines for autonomous agents.

Responsibilities

• Design, build, and scale enterprise Knowledge Graph platforms for large organizations.
• Lead graph-enabled AI infrastructure including RAG and tool-calling interfaces.
• Architect data layers, ensure governance, and collaborate across teams for scale.

Skills

Graph Architecture

Agentic AI

RAG Engineering

Distributed Systems

Knowledge Graph

Education

PhD or Master’s degree in Technology or CS

Tools

Neo4j

Stardog

Cypher

SPARQL

OWL 2

SHACL

GDS

Kafka

Job description

Sr. Consultant Machine Learning & Knowledge Graph Engineer

Data Science is all about breaking new ground to enable businesses to answer their most urgent questions. Pioneering massively parallel data-intensive analytic processing, our mission is to develop a whole new approach to generating meaning and value from petabyte-scale data sets and shape brand new methodologies, tools, statistical methods and models. What’s more, we are in collaboration with leading academics, industry experts and highly skilled engineers to equip our customers to generate sophisticated new insights from the biggest of big data.

Join usto do the best work of your career and make a profound impact asSr. Consultant Machine Learning & Knowledge Graph Engineeron our growing and dynamic team inRound Rock, Texas.

Whatyou’llachieve

Lead the architecture, development, and deployment of enterprisescale ML solutions across Dell’s global ecosystem.DriveMLOpsstandards, buildproductiongradeML services, and collaborate across engineering, product, and platform teams to enable AI atscale. ScaleML solutions across Dell’s global ecosystem. As a Sr. Consultant Machine Learning & Knowledge Graph Engineer, you will play a pivotal role in advancing our AI and ML capabilities and creating Enterprise wide KG marketplace and Ontology layouts. This is a high-impact, enterprise-level technical leadership position responsible for defining and executing Dell's graph data strategy. You will architect production-grade Knowledge Graph platforms, design semantic data layers that power Agentic AI, and drive the convergence of graph technologies with large-scale data engineering ecosystems. This role demands a rare combination of deep graph expertise, distributed systems mastery, and strategic business influence.

You will

• Knowledge Graph Architecture and Delivery: Design, build, and scale enterprise Knowledge Graph platforms using Neo4j and/or Stardog, establishing graph-native data models that enable entity resolution, relationship discovery, and semantic reasoning across business domains. Ontology and Semantic Layer Engineering: Define and govern enterprise ontologies (OWL 2), taxonomies, and semantic schemas that provide a unified, machine-interpretable view of Dell's data assets, ensuring consistency, reusability, and inferencing capability

• Graph-Powered Agentic AI Infrastructure: Architect graph-backed Retrieval-Augmented Generation (RAG) systems, tool-calling interfaces, and dynamic prompt-to-graph query pipelines that fuel autonomous AI agent decision-making with deterministic, explainable knowledge. Data Virtualization and Federation: Lead the design of virtualized graph layers using Stardog Virtual Graphs or equivalent federation patterns, enabling real-time querying across SQL, NoSQL, and streaming data sources without mass ETL

• Graph Data Science and Analytics: Operationalize advanced graph algorithms — community detection, centrality analysis, node embeddings (Node2Vec, FastRP), link prediction — using Neo4j GDS or equivalent libraries to extract actionable intelligence from connected data. Real-Time Graph Ingestion and Streaming: Design high-throughput, low-latency graph ingestion pipelines integrating Kafka, Spark Structured Streaming, and graph-native CDC mechanisms to maintain continuously updated knowledge representations

• Enterprise Graph Governance: Establish comprehensive graph data governance frameworks including SHACL/SHEX constraint validation, RBAC-based graph security models, data lineage tracking, and ontology versioning strategies. Cross-Functional Strategic Partnership: Collaborate with Principal Data Scientists, AI/ML platform teams, product leaders, and executive stakeholders to identify high-value graph use cases and translate complex business problems into graph-solvable architectures

• Technology Evaluation and Innovation: Continuously evaluate emerging graph technologies (GQL/ISO standards, vector-graph hybrid search, graph neural networks, LLM-to-graph interfaces) and provide executive-level recommendations on adoption. Mentorship and Engineering Culture: Serve as the technical anchor and mentor for Senior Advisors, Staff Engineers, and tech leads, cultivating deep graph expertise across the organization and driving a culture of engineering excellence and innovation

• Mentorship and Engineering Culture: Serve as the technical anchor and mentor for Senior Advisors, Staff Engineers, and tech leads, cultivating deep graph expertise across the organization and driving a culture of engineering excellence and innovation

Take the First Step Towards Your Dream Career

Every Dell Technologies team member brings something unique to the table.Here’swhat we are looking for with this role:

Essential Requirements:

• Graph Architecture Mastery: Extensive hands-on experience designing and operating production-grade graph systems using Neo4j (Cypher, GDS, APOC, AuraDB, Causal Clustering) and/or Stardog (SPARQL, OWL 2 reasoning, Virtual Graphs, SHACL validation) along with Ontology and Semantic Modeling: Proven expertise in enterprise ontology engineering — OWL 2 profiles, RDF/RDFS, SKOS taxonomies, property graph modeling patterns, and schema evolution strategies at scale

• Agentic AI and RAG Engineering: Deep practical understanding of building graph-backed data environments for autonomous AI agents, including knowledge retrieval pipelines, tool-calling orchestration, dynamic SPARQL/Cypher generation from natural language, and hybrid vector-graph search architectures

• Distributed Systems and Data Scale: Expert-level command over PySpark, Kafka, data lakehouses (Apache Iceberg, Delta Lake), and enterprise orchestration (Airflow), with proven ability to integrate these with graph ecosystems and programming and query proficiency: Advanced fluency in Python, SQL, Cypher, and SPARQL, with strong software engineering practices (CI/CD, testing, version control, containerization)

• Graph Data Science: Hands-on experience operationalizing graph algorithms — PageRank, Louvain, Label Propagation, node embedding techniques — and integrating graph-derived features into downstream ML/AI pipelines

• Experience: 12+ years of progressive experience in data engineering, graph architecture, and cloud-native platform delivery, with at least 4+ years focused specifically on Knowledge Graph or semantic technology initiatives at enterprise scale

• Strategic Leadership: Exceptional communication, advisory, and stakeholder-management skills, with a demonstrated history of driving large-scale technical transformations and influencing cross-functional technology strategy

Desirable Requirements

• PhD orMaster's degree in Technology, Computer Science, MachineLearningor equivalent quantitative field

• Experience in data mesh or data fabric architectures with graph as the metadata backbone.