Data Architect, AI

Overview

THE ROLE: Enable scalable, efficient, and reliable AI/ML initiatives by designing and implementing robust data architectures that support artificial intelligence workloads. This role exists to build the foundational data infrastructure that powers AI solutions, ensuring data quality, accessibility, governance, and performance for machine learning pipelines. The AI Data Architect bridges data engineering and AI/ML requirements, creating architectures that support both current AI needs and future innovation.

• Design end-to-end data architectures specifically optimized for AI/ML workloads and use cases
• Develop data strategies that support AI initiatives, including data acquisition, storage, processing, and serving
• Architect scalable data pipelines for ingesting, redefining, and preparing data for machine learning
• Design feature stores and data platforms that enable efficient feature engineering and model training
• Implement data quality frameworks and monitoring to ensure high-quality training and inference data
• Establish data governance practices for AI/ML, including metadata management, lineage tracking, and versioning
• Design storage solutions optimized for AI workloads, considering performance, cost, and scalability
• Architect real-time and batch data processing systems to support various ML use cases
• Collaborate with data scientists to understand data requirements and optimize data access patterns
• Implement MLOps data infrastructure, including model training pipelines, experiment tracking, and model registries
• Evaluate and select appropriate technologies for AI data infrastructure (databases, data lakes, processing frameworks)
• Ensure data security, privacy, and compliance for AI/ML systems, including sensitive data handling
• Design monitoring and observability solutions for data pipelines and ML data flows
• Optimize data infrastructure costs while maintaining performance and reliability
• Document data architectures, data flows, and build patterns for team reference
• Make strategic decisions on technology choices, architectural patterns, and infrastructure design for AI/ML systems

• 7+ years of progressive experience in data engineering and architecture with significant focus on AI/ML infrastructure; proven success designing large-scale data systems
• Data architecture and modeling for AI/ML workloads
• Data engineering and ETL/ELT pipeline development
• Data processing technologies (Spark, Kafka, Airflow, Hadoop ecosystem)
• Cloud data platforms (AWS, Azure, Google Cloud data services)
• Database systems (SQL, No
  
  SQL, vector databases, graph databases)
• Proven track record of designing and implementing scalable data architectures for AI/ML
• Deep understanding of data requirements for different types of ML models and workflows
• Experience with feature engineering infrastructure and feature stores
• Solid understanding of data governance, quality, and security practices
• Ability to balance technical requirements with business needs and constraints
• MLOps platforms and practices (Kubeflow, MLflow, Sage Maker)
• Real-time streaming architectures for AI applications
• Data versioning and lineage tools (DVC, Great Expectations)
• Vector databases for AI applications (Pinecone, Weaviate, Milvus)
• Container orchestration (Kubernetes, Docker)
• Infrastructure as code (Terraform, Cloud Formation)
• Understanding of ML model requirements and deployment patterns
• Experience with data privacy techniques (differential privacy, federated learning)

• Minimum:
  Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related technical field
• Preferred:
  Master's degree in Computer Science, Data Science, or related field; combination of deep technical expertise and strategic thinking
• Certifications:
  
  Cloud architect certifications (AWS Solutions Architect, Azure Solutions Architect, Google Cloud Architect), data engineering certifications valuable
• Equivalent
  
  Experience:
  
  10+ years of hands-on experience designing and implementing data architectures for AI/ML with proven track record of scalable systems may substitute for advanced degree

Herbalife offers a variety of benefits to eligible employees in the U.S. (limited to the 50 States and the District of Columbia), which includes Group Health Programs, other Voluntary Benefit Programs, and Paid Time Off. Group Health Programs include Medical, Dental, Vision, Health Savings Account (HSA), Flexible Spending Accounts (FSA), Basic Life/AD&D;
Short-Term and Long-Term Disability, and an Employee Assistance Program (EAP). Other Voluntary Benefit Programs include a 401(k) plan, Wellness Incentive Program, Employee Stock Purchase Plan (ESPP), Supplemental Life/Critical Illness/Hospitalization/Accident Insurance, and Pet Insurance. Paid time off includes Company-observed U.S. Holidays, Floating Holidays, Vacation, Sick Time, a Volunteer Program, Paid Maternity and Paternity Leave, Bereavement Leave, Personal Leave and time off for voting.