Sr. Consultant Machine Learning & Knowledge Graph Engineer

Dell Technologies•TX, TX

2d•Onsite

About The Position

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 us to do the best work of your career and make a profound impact as Sr. Consultant Machine Learning & Knowledge Graph Engineer on our growing and dynamic team in Round Rock, Texas. What you’ll achieve Lead the architecture, development, and deployment of enterprise scale ML solutions across Dell’s global ecosystem. Drive MLOps standards, build production grade ML services, and collaborate across engineering, product, and platform teams to enable AI at scale. Scale ML 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 Take the First Step Towards Your Dream Career Every Dell Technologies team member brings something unique to the table. Here’s what we are looking for with this role:

Requirements

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)
Proven expertise in enterprise ontology engineering — OWL 2 profiles, RDF/RDFS, SKOS taxonomies, property graph modeling patterns, and schema evolution strategies at scale
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
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
Advanced fluency in Python, SQL, Cypher, and SPARQL, with strong software engineering practices (CI/CD, testing, version control, containerization)
Hands-on experience operationalizing graph algorithms — PageRank, Louvain, Label Propagation, node embedding techniques — and integrating graph-derived features into downstream ML/AI pipelines
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
Exceptional communication, advisory, and stakeholder-management skills, with a demonstrated history of driving large-scale technical transformations and influencing cross-functional technology strategy

Nice To Haves

PhD or Master's degree in Technology, Computer Science, Machine Learning or equivalent quantitative field
Experience in data mesh or data fabric architectures with graph as the metadata backbone.

Responsibilities

Lead the architecture, development, and deployment of enterprise scale ML solutions across Dell’s global ecosystem.
Drive MLOps standards, build production grade ML services, and collaborate across engineering, product, and platform teams to enable AI at scale.
Scale ML solutions across Dell’s global ecosystem.
Play a pivotal role in advancing our AI and ML capabilities and creating Enterprise wide KG marketplace and Ontology layouts.
Define and execute Dell's graph data strategy.
Architect production-grade Knowledge Graph platforms.
Design semantic data layers that power Agentic AI.
Drive the convergence of graph technologies with large-scale data engineering ecosystems.
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.
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.
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.
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.
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.
Design high-throughput, low-latency graph ingestion pipelines integrating Kafka, Spark Structured Streaming, and graph-native CDC mechanisms to maintain continuously updated knowledge representations.
Establish comprehensive graph data governance frameworks including SHACL/SHEX constraint validation, RBAC-based graph security models, data lineage tracking, and ontology versioning strategies.
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.
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.
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.