AI Data Architect

Overview We’re seeking an experienced AI Data Architect to design and scale data/AI platforms that enable robust machine learning workflows and production-grade AI services. You will own the architecture for data pipelines, model-ready datasets, access controls, and retrieval-based generation (RAG) systems, working closely with ML engineers, data engineers, and security stakeholders. Responsibilities - Design end-to-end data architecture for AI/ML use cases, including ingestion, transformation, feature/data preparation, and dataset versioning - Build and optimize scalable ML data pipelines on AWS to support training, evaluation, and inference workloads - Own RAG architecture: data indexing, chunking strategies, embedding management, retrieval, and prompt/response integration patterns - Define IAM roles, permissions, and secure access patterns for data, model artifacts, and vector stores - Collaborate with engineers to implement data observability (lineage, quality checks, monitoring, and alerting) for AI pipelines - Ensure reliability and performance by tuning storage formats, query patterns, and pipeline throughput - Create architecture documentation and standards for reproducibility, scalability, and cost control - Partner with stakeholders to translate business needs into technical design and measurable success criteria Requirements - 5+ years of experience designing data and/or AI platforms in production environments - Strong hands-on experience with AWS (e.g., S3, IAM, and ML/data services) and building scalable data pipelines - Solid understanding of AI/ML concepts and practical experience integrating ML workflows with production data systems - Demonstrated experience with RAG systems, including embeddings/vector search and retrieval pipeline design - Deep knowledge of IAM concepts and secure engineering practices (least privilege, role-based access, and auditability) - Proficiency with data modeling and working with large-scale datasets - Experience with deploying or supporting AI/ML services and handling data lifecycle (retention, access, and governance) Nice to have - Experience with vector databases or managed vector search solutions in AWS ecosystems - Knowledge of LLM orchestration frameworks and production RAG evaluation methods - Familiarity with data governance, PII handling, and compliance-oriented design - Experience with CI/CD and infrastructure-as-code for ML/data deployments