Enterprise AI Architect — Multi-Cloud & Hybrid Platforms

Stanford University; United States; On-site; USD 140,000 - 210,000; Full time

Job summary

SLAC National Accelerator Laboratory is seeking an AI Specialist to lead end-to-end AI/ML projects across AWS, GCP, and hybrid environments. You will collaborate with researchers, engineers, and platform teams to build secure, scalable AI services for scientific research and operations.

The role emphasizes responsible AI practices, governance, and rapid transition from PoC to production, with a focus on reusable platforms and scalable architectures.

Qualifications

• Bachelor's degree or equivalent plus extensive experience in AI/ML.
• Experience designing, deploying and supporting AI/ML in production cloud environments.
• Proficiency with AWS and GCP AI/ML services and hybrid architectures.

Responsibilities

• Lead end-to-end development and operationalization of AI/ML solutions across AWS, GCP and hybrid environments, including data ingestion, model development, deployment, and lifecycle management.
• Collaborate with researchers, business stakeholders, data scientists, software engineers, cybersecurity, DevOps and platform teams to translate requirements into scalable AI solutions.
• Design data pipelines and deployment architectures using cloud-native services and governance practices.
• Develop generative AI applications, including retrieval-augmented generation, prompts, evaluation, and AI agents.
• Establish MLOps/LLMOps capabilities with CI/CD, testing, monitoring, drift detection, and governance.

Skills

AWS

GCP

Python

ML/AI

MLOps

Education

Bachelor's degree in CS/Engineering/Data Science

Tools

Docker

Kubernetes

Terraform

Job description

SLAC National Accelerator Laboratory is seeking an AI Specialist to lead end-to-end AI/ML projects across AWS, GCP, and hybrid environments. You will collaborate with researchers, engineers, and platform teams to build secure, scalable AI services for scientific research and operations.

The role emphasizes responsible AI practices, governance, and rapid transition from PoC to production, with a focus on reusable platforms and scalable architectures.