AI Lab Infrastructure Engineer

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Berkeley Research Group's Ai Department is seeking an AI Infrastructure Engineer to lead the development of our Virtual Ai Lab initiative. Following the successful completion of Phase 01 (physical Ai Lab build-out), this role will focus on creating a virtual access layer that makes our high-performance Ai Lab remotely accessible to teams across BRG. The ideal candidate will design and implement scalable infrastructure to support processing 100,000+ documents daily using state-of-the-art LLMs from OpenAI and Anthropic.

About the Role

As an AI Infrastructure Engineer, you will architect and build the virtual access interface for our physical Ai Lab, ensuring secure, scalable, and efficient remote processing capabilities. You will lead the design and implementation of infrastructure that allows BRG teams to leverage our Ai Lab's computational power remotely, while maintaining performance standards for large-scale document processing. Key responsibilities include developing customizable interfaces for different BRG groups, implementing secure access controls, and ensuring optimal resource allocation for concurrent users processing massive datasets through LLMs.

Key Responsibilities

• Design and implement a virtual access layer for the physical Ai Lab infrastructure
• Build scalable remote processing capabilities supporting 100,000+ documents per day
• Create customizable, expandable interfaces for different BRG business units
• Optimize infrastructure for maximum LLM token throughput (OpenAI/Anthropic)
• Implement secure authentication and access management systems
• Ensure high availability and fault tolerance for mission-critical AI workloads
• Lead infrastructure projects from conception to production deployment

Required:

• Bachelor's degree in Computer Science, Information Technology, or a related field
• Minimum six to eight (6-8) years of hands-on experience designing, deploying, and managing scalable cloud infrastructure
• Strong experience with Infrastructure as Code (IaC) tools and methodologies
• Experience designing, implementing, and maintaining scalable, secure, and cost-efficient cloud/on-prem solutions
• Proven ability to manage and lead projects to deliver high-quality, replicable solutions
• Proficiency in VCS (Git/GitHub), modern coding languages (Python, .NET, Java, etc.), Software Development Life Cycle, and CI/CD practices
• Experience with API design and implementation for distributed systems
• Knowledge of GPU infrastructure and optimization for AI workloads
• Hands-on experience with AWS Services including:
• EC2/Lambda (apps/functions)
• SageMaker (ML)
• S3 (file management)