AI Data Architect

Accountabilities

• Owes the design, implementation, governance, and continuous evolution of an enterprise AI data ecosystem.
• Fulfills the role of building scalable, secure, and AI-ready data platforms while enabling teams to deliver reliable intelligent solutions.
• Architects and manages a unified AI data platform that ingests, transforms, stores, governs, and serves data for AI applications across the organization.
• Designs advanced data architectures including data lakes, lakehouses, data meshes, warehouses, and event-driven systems optimized for AI workloads.
• Establishes data models, schemas, contracts, lineage processes, and governance frameworks to ensure accuracy, consistency, and accessibility.
• Buils and optimizes automated data pipelines, ETL processes, reporting solutions, and analytical capabilities using modern data technologies.
• Transforms legacy data environments into cloud-native, AI-ready architectures with improved scalability, performance, and efficiency.
• Develops retrieval infrastructure for RAG-based applications, including embedding pipelines, vector databases, semantic search capabilities, and hybrid retrieval solutions.
• Creates and maintains observability frameworks to monitor AI agent behavior, data quality, retrieval relevance, output accuracy, and system performance.
• Defines architecture standards, engineering practices, reusable components, CI/CD processes, infrastructure automation, and documentation guidelines.
• Ensures strong security, privacy, and access governance for both human users and AI-driven systems.
• Partners with engineering teams to enable the adoption of AI platforms, data standards, and modern development practices.

Requirements

• Bring extensive experience in data architecture, engineering, and AI infrastructure, with a proven ability to design enterprise-scale platforms supporting advanced AI applications.
• Have 15+ years of hands-on experience in data engineering, architecture, and large-scale data platform development.
• Show strong experience designing production AI/ML and LLM-focused data infrastructure.
• Show advanced proficiency in Python and SQL, with experience building complex ETL and data transformation workflows.
• Show experience with cloud technologies including AWS services such as S3, Glue, EKS, Bedrock, Kinesis, and Redshift.
• Show hands-on experience with Docker, Kubernetes, Terraform, GitHub Actions, and modern DevOps practices.
• Show knowledge of AI frameworks and technologies including LangChain, LlamaIndex, LLM APIs, vector databases, and knowledge graphs.
• Show experience with RAG architectures, embeddings, semantic search, vector stores, and retrieval optimization.
• Show understanding of LLMOps practices, including model deployment, monitoring, evaluation frameworks, and AI lifecycle management.
• Show experience with streaming and processing technologies such as Kafka, Spark Structured Streaming, PySpark, and Delta Lake.
• Show familiarity with metadata management, data lineage, data quality platforms, and governance practices.
• Show strong problem-solving skills with the ability to communicate complex technical concepts clearly.
• Show ability to collaborate effectively with cross-functional teams and drive technical standards across engineering organizations.

Benefits

• Medical insurance benefits according to company policy.
• Dental and vision insurance coverage.
• Employer-paid disability, life, and accidental death & dismemberment insurance.
• Unlimited paid time off.
• Paid parental leave.
• 401(k) retirement plan.
• Flexible work policy with remote work opportunities.
• 12 paid holidays.