Consultant Machine Learning & Knowledge Graph Engineer

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 us to do the best work of your career and make a profound impact

as Consultant ML & KG 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 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 will be responsible

for designing, building, and operationalizing machine learning systems,

including next generation agentic and GenAI powered applications. 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

performance ML 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’s what 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 or Master's degree in Technology, Computer Science, Machine Learning or
• equivalent quantitative field
• * Familiarity leveraging graph-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.