Sr. Consultant Machine Learning & Knowledge Graph Engineer

About the role

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. As a Sr. Consultant Machine Learning & Knowledge Graph Engineer, you will play a pivotal role in advancing Dell’s AI and ML capabilities and creating an enterprise-wide Knowledge Graph marketplace and ontology layouts. This is a high-impact, enterprise-level technical leadership position responsible for defining and executing Dell's graph data strategy.

Responsibilities

• 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.

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).
• 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.
• 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.

Qualifications

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