Sr. Consultant Machine Learning & Knowledge Graph Engineer

Dell Technologies•TX, TX

12d

About The Position

Dell Technologies is seeking a senior technical leader to advance its enterprise AI and machine learning capabilities and create enterprise-wide knowledge graph marketplaces and ontology layouts. The role leads the architecture, development, and deployment of enterprise-scale machine learning solutions and defines and executes Dell’s graph data strategy. It involves building production-grade knowledge graph platforms and semantic data layers that support Agentic AI, while connecting graph technologies with large-scale data engineering ecosystems. The position encompasses enterprise knowledge graph architecture, ontology and semantic-layer engineering, graph-backed retrieval-augmented generation and agent infrastructure, data virtualization and federation, graph analytics, real-time graph ingestion, and graph governance. The engineer partners with data science, AI/ML platform, product, and executive teams to identify valuable use cases and translate business challenges into graph-based solutions. The role also evaluates emerging graph technologies, makes adoption recommendations, and mentors senior technical colleagues.

Requirements

Extensive hands-on experience designing and operating production-grade graph systems using Neo4j and/or Stardog, including relevant graph query, analytics, reasoning, clustering, virtualization, and validation capabilities.
Proven expertise in enterprise ontology engineering, including OWL 2 profiles, RDF/RDFS, SKOS taxonomies, property graph modeling patterns, and schema evolution at scale.
Deep practical understanding of graph-backed data environments for autonomous AI agents, including knowledge retrieval pipelines, tool-calling orchestration, natural-language-to-SPARQL/Cypher generation, and hybrid vector-graph search.
Expert-level command of PySpark, Kafka, data lakehouses such as Apache Iceberg and Delta Lake, and orchestration platforms such as Airflow, with ability to integrate them with graph ecosystems.
Advanced fluency in Python, SQL, Cypher, and SPARQL, as well as software engineering practices including CI/CD, testing, version control, and containerization.
Hands-on experience operationalizing graph algorithms such as PageRank, Louvain, Label Propagation, and node embeddings, 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.
At least 4+ years focused specifically on enterprise-scale Knowledge Graph or semantic technology initiatives.
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 an equivalent quantitative field.
Experience in data mesh or data fabric architectures with graph as the metadata backbone.

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, taxonomies, and semantic schemas, including OWL 2 models, to provide consistent, reusable, machine-interpretable views of data assets.
Architect graph-backed RAG systems, tool-calling interfaces, and prompt-to-graph query pipelines to support autonomous AI agent decision-making.
Lead the design of virtualized graph layers and federation patterns that enable real-time queries across SQL, NoSQL, and streaming sources without mass ETL.
Operationalize graph algorithms such as community detection, centrality analysis, node embeddings, and link prediction using Neo4j GDS or equivalent libraries.
Design high-throughput, low-latency graph ingestion pipelines using technologies such as Kafka, Spark Structured Streaming, and graph-native CDC mechanisms.
Establish graph governance frameworks, including SHACL/SHEX constraint validation, RBAC-based graph security, data lineage, and ontology versioning.
Collaborate with Principal Data Scientists, AI/ML platform teams, product leaders, and executive stakeholders to identify graph use cases and develop graph-based architectures.
Evaluate emerging graph technologies and provide executive-level adoption recommendations.
Mentor Senior Advisors, Staff Engineers, and technical leads, and cultivate graph expertise and engineering excellence.
Lead architecture, development, and deployment of enterprise-scale ML solutions; establish MLOps standards and build production-grade ML services.
Collaborate across engineering, product, and platform teams to scale ML solutions across Dell’s global ecosystem.