Senior AI Platform Architect & Tech Leader

Teradata Corporation (SE); Bismarck (ND); On-site; USD 200,000 - 299,000; Full time

Job summary

Teradata seeks a Senior Staff AI Engineer to lead the design and evolution of enterprise AI platforms, owning architecture decisions and guiding cross-team initiatives. You will drive large-scale AI systems with LLMs, embeddings, vector stores, and AI orchestration, partnering with product and architecture leaders to raise technical standards across teams.

This role emphasizes autonomy, mentorship, and delivering secure, scalable services across AWS/Azure/GCP, with a flexible work model and

Qualifications

• Bachelor’s degree in CS, Engineering, or equivalent practical experience.
• 12+ years of experience building backend services, distributed systems, or data/AI platforms.
• Strong proficiency in Java, Go, or Python, with experience building largescale services.
• Deep understanding of distributed system design, scalability and cloud-native architectures.
• Proven experience designing and operating production systems with SQL and NoSQL data stores.

Responsibilities

• Lead design and evolution of largescale, distributed AI systems powering Teradata’s platforms.
• Own end-to-end architecture for AI capabilities like agentic workflows, RAG pipelines, vector search.
• Drive technical strategy and consistency across engineering teams.

Skills

Java

Go

Python

Distributed systems

Cloud native architectures

Education

Bachelor’s degree in Computer Science or equivalent

Tools

Kubernetes

Docker

SQL/NoSQL data stores

Job description

Teradata seeks a Senior Staff AI Engineer to lead the design and evolution of enterprise AI platforms, owning architecture decisions and guiding cross-team initiatives. You will drive large-scale AI systems with LLMs, embeddings, vector stores, and AI orchestration, partnering with product and architecture leaders to raise technical standards across teams.

This role emphasizes autonomy, mentorship, and delivering secure, scalable services across AWS/Azure/GCP, with a flexible work model and