AI Data Architect in United States Embassy at Jobgether

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.

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.

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.

• 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.