Sr. Consultant Machine Learning & Graph Engineer

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, No
  
  SQL, and streaming data sources without mass ETL
• Graph Data Science and Analytics:
  Operationalize advanced graph algorithms — community detection, centrality analysis, node embeddings (Node2
  
  Vec, 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…