Consultant Machine Learning & Knowledge Graph Engineer

7 days ago

Round Rock, TX, USAStaff+

H1B Sponsor

Responsibilities

Lead the architecture, prototyping, development, deployment, and delivery of autonomous AI agents, ML systems, pipelines, and inference services.
Drive MLOps practices including model monitoring, drift detection, automated retraining, and production operational support.
Design and scale enterprise Knowledge Graph platforms using Neo4j and/or Stardog for entity resolution, relationship discovery, and semantic reasoning.
Define and govern enterprise ontologies, taxonomies, and semantic schemas using OWL 2.
Architect graph-backed Retrieval-Augmented Generation systems, tool-calling interfaces, and prompt-to-graph query pipelines.
Integrate graph ecosystems with data pipelines, distributed infrastructure, governance systems, and large-scale data engineering platforms.
Provide technical leadership and drive Dell’s broader AI/ML strategy across engineering, product, platform, and business teams.

12+ years of experience delivering complex AI/ML or applied science systems, including deep learning, machine learning, and LLM-based solutions.
Advanced Python expertise and strong knowledge of ETL pipelines, with Airflow preferred, and modern data-warehousing concepts.
Extensive hands-on experience designing and operating production-grade graph systems with Neo4j and/or Stardog, including their associated query, reasoning, clustering, and validation technologies.
Expert command of PySpark, Kafka, Apache Iceberg, Delta Lake, and Airflow, with experience integrating them into graph ecosystems.
Strong software engineering experience with AI frameworks, cloud environments, Docker, Kubernetes, and AWS, GCP, or Azure.
Experience with training, fine-tuning, and applying LLMs for agentic AI applications.
Experience or familiarity with graph-based techniques, semantic search, hybrid search, traditional information retrieval, machine learning models, search relevance, and large-scale telemetry data handling.
PhD or Master’s degree in Technology, Computer Science, Machine Learning, or an equivalent quantitative field is desirable.

10,000+ employees

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