PRINCIPAL AI DATA ARCHITECT @ VIRIDIAN TECH INC
The Principal AI Data Architect role is a senior-level position dedicated to architecting and owning enterprise AI-ready data platforms as a single source of truth. The successful candidate will design innovative lakehouse and data mesh architectures specifically optimized for AI workloads, ensuring the efficient flow of information through real-time and batch pipelines using technologies such as Kafka, Spark, and Databricks. You will also be responsible for developing semantic models, knowledge graphs, and feature stores that empower AI consumers including agents, chatbots, and ML models.
In addition to technical architecture, this role involves a strong emphasis on data governance, security, and compliance with frameworks like SOX, GDPR, and SOC2. You will implement robust ML/LLMOps pipelines utilizing MLflow and CI/CD practices while building AI observability and evaluation frameworks. By defining data contracts and architectural standards, you will ensure that the infrastructure supports large-scale RAG, vector search, and retrieval systems effectively within a PST time zone working environment.
In addition to technical architecture, this role involves a strong emphasis on data governance, security, and compliance with frameworks like SOX, GDPR, and SOC2. You will implement robust ML/LLMOps pipelines utilizing MLflow and CI/CD practices while building AI observability and evaluation frameworks. By defining data contracts and architectural standards, you will ensure that the infrastructure supports large-scale RAG, vector search, and retrieval systems effectively within a PST time zone working environment.
Key Requirements
Minimum of 15 years of experience in data architecture or related fields
Advanced proficiency in Databricks, Delta Lake, and PySpark
Expertise in building real-time and batch pipelines using Kafka and Spark
Proven experience designing Data Lakehouse and Data Mesh architectures
In-depth knowledge of ML/LLMOps pipelines including MLflow and CI/CD
Hands-on experience with Vector Databases such as Pinecone, FAISS, or ChromaDB
Familiarity with Knowledge Graphs and tools like Neo4j
Strong programming skills in Python and SQL
Deep experience with cloud infrastructure on AWS or Azure
Solid understanding of data governance, security, and compliance frameworks