Consultant Machine Learning Engineer & AI Product Lead r

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 a Consultant Machine Learning Engineer & AI Product Lead on our growing and dynamic team in Round Rock, Texas.

What you'll achieve
Lead the technical vision, product roadmap, and end-to-end delivery of enterprise-scale machine learning solutions across Dell's global ecosystem. As a Consultant Machine Learning Engineer & AI Product Lead, you will sit at the intersection of advanced machine learning engineering, rapid scripting and automation, product management, and semantic data integration. You will drive AI products from discovery and proof-of-concept through to production deployment, translating complex business problems into actionable technical roadmaps and robust ML systems. In addition to building high-performance ML pipelines, production-grade inference services, and automation scripts, you will leverage knowledge graph technologies to enhance data context, retrieval, and decision-making across our AI ecosystem.

You will

• Lead Design and development of activities associated with software using a range of applicable technologies (e.g., programming languages, compilers, assemblers, debuggers, other tools) for products used in local, networked or Internet-related computer programs.
• Drive the product strategy and technical roadmap for enterprise AI/ML initiatives, collaborating with business stakeholders and engineering teams to define & determine software & product requirements, technical specifications, and system impacts
• Develop high availability and highly scalable applications which will be used by both internal and external customers.
• Create technical documentation and functional test use cases.
• Analyze business requirements, processes, data, and system integration points to determine and recommend appropriate technology solutions.
• Establish and champion MLOps best practices, including continuous integration/continuous deployment (CI/CD), automated retraining pipelines, drift detection, and observability
• Work closely with the global development team, senior Solution Architects, and executives within the Engineering group and other functions to plan ongoing feature development, product maintenance, and development team management

  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:

• 10+ years of professional experience in machine learning engineering, applied data science, or technical AI product leadership delivering enterprise-grade ML solutions.
• Strong product management acumen with proven experience leading technical product lifecycles, defining product roadmaps, writing functional/technical specifications, and managing cross-functional stakeholder alignments.
• Advanced scripting and programming proficiency in Python and Shell/Bash for rapid prototyping, automation, data engineering, and production service development.
• Demonstrated experience with MLOps frameworks, containerization (Docker, Kubernetes), workflow orchestration (Airflow, Kubeflow), and cloud-native deployments (AWS, Azure, or GCP).
• Desirable Requirements:
• Master's degree or PhD in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field.
• Experience acting as a Technical Product Manager (TPM) or lead product owner in an agile ML/AI engineering environment.
• Familiarity with graph query languages (Cypher, SPARQL), ontology modeling, or leveraging Graph Neural Networks (GNNs) alongside traditional predictive modeling