General Motors
Staff Cloud and AI Solutions Architect
$189K–$291K
Warren, Michigan, United States of America +1 · Remote/Hybrid · Staff · Full-time · Posted Sep 18, 2026
- Location
- Warren, Michigan, United States of America
Sunnyvale, California, United States of America - Experience
- 10+ years of professional software engineering experience.
- Education
- Bachelor’s degree in a related technical field; equivalent practical experience may be considered.
About the job
Leads the architecture and hands-on delivery of scalable cloud and data foundations for mapping systems, while developing production-ready AI agents and workflows for engineering teams.
What you'll do
- Define and build scalable mapping data platforms, cloud services, pipelines, APIs, and data-quality and governance practices.
- Design, productionize, and scale knowledge-grounded Agentic AI solutions and workflows for engineering tasks.
- Set technical direction across teams through architecture reviews, reusable frameworks, collaboration, and mentoring.
What they ask for
- 10+ years of professional software engineering experience building and operating production systems.
- Senior technical leadership across architecture, design, implementation, and production operations.
- Hands-on experience with a major cloud platform and cloud-native distributed systems, including infrastructure as code, containers or Kubernetes, CI/CD, observability, and cloud security.
- Experience designing data platforms or pipelines and applying AI or workflow automation to practical problems.
Nice to have
- Experience in automotive, mapping, geospatial, robotics, simulation, or another safety- and scale-sensitive domain.
- Experience with Azure, Databricks, Terraform, Kubernetes, Spark or PySpark, Kafka, vector databases, or knowledge-grounded AI.
Pay
$189,300 to $290,700 (base salary)
Tools
Azure, AWS, Google Cloud Platform, Python, Go, Java, C++, Kubernetes
AI focus
Apply AI, generative AI, retrieval-augmented generation, and agentic workflows to engineering tasks; build knowledge-grounded agents with retrieval, tool use, orchestration, access controls, evaluation, observability, human oversight, and safe deployment.
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