Consultant Machine Learning & Knowledge Graph Engineer

Dell; Round Rock (TX); On-site; USD 150,000 - 210,000; Full time

Job summary

Dell Technologies in Round Rock, Texas seeks a Consultant Machine Learning & Knowledge Graph Engineer to lead the architecture, development, and deployment of enterprise-scale ML solutions. You will drive MLOps standards and collaborate across engineering, product, and platform teams to enable AI at scale.

You will build and operationalize Knowledge Graph platforms, design semantic data layers, and advance agentic AI with deterministic, explainable knowledge.

Qualifications

• 12+ years delivering AI/ML or applied science systems.
• Advanced Python with ETL/pipeline knowledge.
• Experience with production-grade graph systems (Neo4j/Stardog).
• Experience with Docker, Kubernetes and cloud platforms (AWS/GCP/Azure).

Responsibilities

• Lead architecture, development and deployment of enterprise ML solutions.
• Define MLOps standards including CI/CD and monitoring.
• Design and scale Knowledge Graph platforms and ontologies.
• Architect RAG systems and agentic AI pipelines.

Skills

Python programming

Distributed systems

AI/ML proficiency

Graph theory / knowledge graphs

Education

PhD or Master’s in Tech/CS/ML

Tools

Neo4j

Stardog

PySpark

Kafka

Airflow

Docker

Kubernetes

AWS/GCP/Azure

Job description

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 asConsultantML & KG 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 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. You willbe responsible fordesigning, building, and operationalizing machine learning systems, includingnext generationagentic andGenAI poweredapplications. You will drive and execute our broader AI/ML strategy.You will also be responsible to 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 work deeply across data pipelines, model development, optimization, and production deployment to deliver scalable,high performanceML solutions.

You will

• Lead the end-to-end Agentic lifecycle—from conceptualizing, prototyping and driving delivery with engineering teams and design and build autonomous AI agents, ML systems, pipelines, and inference services.
• Work with business leads to imagine agentic products and drive accelerated delivery through Spec Driven Development and implement MLOps practices including CI/CD, model monitoring, drift detection, and automated retraining.
• Collaborate with Data Engineering and Platform teams to ensure data, infrastructure, and governance readiness along with providing technical leadership while integrating emerging AI/ML technologies and managing production incidents.
• 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

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

• 12+ years of experience delivering complex AI/ML or applied science systems, including deep learning, machine learning, and LLM-based solutions.
• Advanced Python expertise with strong knowledge of ETL pipelines (Airflow preferred) and modern data-warehousing concepts.
• 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)
• 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
• Strong software engineering background with hands-on experience in AI frameworks, cloud environments, and domains such as ML, NLP, IR, recommender systems, and LLMs and proven experience with Docker, Kubernetes, and major cloud platforms (AWS/GCP/Azure), including training, fine-tuning, and applying LLMs for agentic AI applications.

Desirable Requirements

• PhD orMaster's degree in Technology, Computer Science, MachineLearningor equivalent quantitative field
• Familiarity leveraginggraph-based techniques, semantic search, hybrid search systems,and implementing solutions that combine traditional IR methods with machine learning models to enhance search relevancy accuracy and efficiency.Familiarity with large scale data handling when dealing with telemetry systems.