AI Data Architect

• **This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a AI Data Architect based in United States.**
• This role offers the opportunity to design and lead the data foundation behind next-generation artificial intelligence solutions.
• You will architect enterprise-scale AI data platforms that power intelligent agents, RAG applications, predictive models, and advanced analytics capabilities.
• Working at the intersection of data engineering, cloud architecture, and AI innovation, you will shape scalable systems that enable smarter business decisions.
• The position requires deep technical expertise, strategic thinking, and the ability to establish standards for secure, reliable, and high-performing AI infrastructure.
• You will collaborate with engineering teams and stakeholders to modernize legacy data environments and build future-ready architectures.
• This is a high-impact opportunity to define how organizations leverage data to unlock the full potential of AI technologies.

### Accountabilities

The AI Data Architect will own the design, implementation, governance, and continuous evolution of an enterprise AI data ecosystem. The role focuses on building scalable, secure, and AI-ready data platforms while enabling teams to deliver reliable intelligent solutions.

• Architect and manage a unified AI data platform that ingests, transforms, stores, governs, and serves data for AI applications across the organization.
• Design advanced data architectures including data lakes, lakehouses, data meshes, warehouses, and event-driven systems optimized for AI workloads.
• Establish data models, schemas, contracts, lineage processes, and governance frameworks to ensure accuracy, consistency, and accessibility.
• Build and optimize automated data pipelines, ETL processes, reporting solutions, and analytical capabilities using modern data technologies.
• Lead modernization efforts by transforming legacy data environments into cloud-native, AI-ready architectures with improved scalability, performance, and efficiency.
• Develop retrieval infrastructure for RAG-based applications, including embedding pipelines, vector databases, semantic search capabilities, and hybrid retrieval solutions.
• Create and maintain observability frameworks to monitor AI agent behavior, data quality, retrieval relevance, output accuracy, and system performance.
• Define architecture standards, engineering practices, reusable components, CI/CD processes, infrastructure automation, and documentation guidelines.
• Ensure strong security, privacy, and access governance for both human users and AI-driven systems.
• Partner with engineering teams to enable the adoption of AI platforms, data standards, and modern development practices.

## Requirements

The ideal candidate brings extensive experience in data architecture, engineering, and AI infrastructure, with a proven ability to design enterprise-scale platforms supporting advanced AI applications.

• 15+ years of hands-on experience in data engineering, architecture, and large-scale data platform development.
• Strong experience designing production AI/ML and LLM-focused data infrastructure.
• Expertise with data architecture patterns, including data lakes, data warehouses, data hubs, and event-driven architectures.
• Advanced proficiency in Python and SQL, with experience building complex ETL and data transformation workflows.
• Strong experience with platforms such as Snowflake or Databricks, with exposure to both preferred.
• Experience with cloud technologies including AWS services such as S3, Glue, EKS, Bedrock, Kinesis, and Redshift.
• Hands-on experience with Docker, Kubernetes, Terraform, GitHub Actions, and modern DevOps practices.
• Knowledge of AI frameworks and technologies including LangChain, LlamaIndex, LLM APIs, vector databases, and knowledge graphs.
• Experience with RAG architectures, embeddings, semantic search, vector stores, and retrieval optimization.
• Understanding of LLMOps practices, including model deployment, monitoring, evaluation frameworks, and AI lifecycle management.
• Experience with streaming and processing technologies such as Kafka, Spark Structured Streaming, PySpark, and Delta Lake.
• Familiarity with metadata management, data lineage, data quality platforms, and governance practices.
• Strong problem-solving skills with the ability to communicate complex technical concepts clearly.
• 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.