PRINCIPAL AI DATA ARCHITECT @ VIRIDIAN TECH INC
The Principal AI Data Architect role involves architecting and owning an enterprise-grade AI-ready data platform, serving as the single source of truth for the organization. The successful candidate will design sophisticated lakehouse and data mesh architectures specifically tailored for AI workloads, while building robust real-time and batch pipelines using technologies like Kafka, Spark, and Databricks. You will be responsible for developing semantic models, knowledge graphs, and feature stores to support advanced data needs.
Additionally, this position requires designing RAG (Retrieval-Augmented Generation) systems, vector search, and retrieval infrastructure. You will implement ML/LLMOps pipelines utilizing MLflow, CI/CD, and monitoring tools to enable data for AI consumers such as agents, chatbots, and ML models. A critical part of the role includes enforcing data governance, RBAC, and compliance standards like SOX and GDPR. Candidates must be based in the United States and willing to work in the PST time zone.
Additionally, this position requires designing RAG (Retrieval-Augmented Generation) systems, vector search, and retrieval infrastructure. You will implement ML/LLMOps pipelines utilizing MLflow, CI/CD, and monitoring tools to enable data for AI consumers such as agents, chatbots, and ML models. A critical part of the role includes enforcing data governance, RBAC, and compliance standards like SOX and GDPR. Candidates must be based in the United States and willing to work in the PST time zone.
Key Requirements
At least 15+ years of professional experience in data architecture.
Expertise in Databricks, Delta Lake, and PySpark for data processing.
Proficiency in Kafka for real-time and batch pipeline development.
Proven experience with Data Lakehouse and Data Mesh architectures.
Strong understanding of ML/LLMOps pipelines using MLflow and CI/CD.
Hands-on experience with Vector Databases such as Pinecone, FAISS, or ChromaDB.
Knowledge of Knowledge Graphs and graph databases like Neo4j.
Advanced proficiency in Python and SQL for data engineering tasks.
Experience with cloud infrastructure on AWS or Azure, including S3, Glue, and EKS.
Familiarity with containerization and orchestration tools like Docker and Kubernetes.
Experience implementing Infrastructure as Code using Terraform.
Deep knowledge of data governance, RBAC, and compliance frameworks like SOX or GDPR.