AI Platform Engineer

The AI Platform Engineer is responsible for deploying and managing the computing and storage platform backbone that powers AI models. Working on-site alongside AI Data Engineering, IT Infrastructure, Security, and business stakeholders, this role is central to the reliability, performance, and scalability of our AI/ML infrastructure.

• Provision and maintain CPU and GPU compute clusters, high-bandwidth network equipment, and scalable, high-throughput storage systems for AI/ML training and inference, working on-site and virtually to coordinate hands-on configuration and troubleshooting with infrastructure teams.
• Develop and maintain scripts and infrastructure-as-code (IaC) modules for automated configuration of AI/ML infrastructure using tools such as Terraform and Ansible; participate in in-person and virtual code and design reviews with the platform engineering team.
• Develop and maintain CI/CD pipelines and container orchestration platforms (Kubernetes, Flux); troubleshoot pipeline failures and elevate to more senior engineers or other teams as needed, collaborating in person and virtually to resolve issues quickly and effectively.
• Set up and monitor data stores (file systems, block storage, traditional and vector databases) to support LLMs and RAG pipelines, with regular on-site coordination with AI Data Engineering teams to ensure data infrastructure meets evolving model requirements.
• Monitor system performance and log metrics to ensure reliability and uptime; implement system improvements under guidance of more senior engineers, including attending in-person and virtual planning and incident review sessions.
• Collaborate in person with AI Data Engineering, IT Infrastructure, Security, and Business Leaders to optimize infrastructure for performance and cost, including participating in cross-functional working sessions, architecture reviews, and stakeholder briefings.
• Deploy and maintain Model Context Protocol (MCP) connectors and ensure secure, scalable infrastructure for MCP integrations, coordinating directly with security and engineering teams to validate configurations and address emerging requirements.

• At least two (2) years of experience working in an infrastructure, Dev Ops, or platform engineering role, with at least some exposure to AI/ML workloads.
• Proficiency in cloud computing, container orchestration, and infrastructure-as-code.
• Knowledge of networking and distributed storage;
• Experience with monitoring and observability tools to track performance and system health.
• Ability to troubleshoot complex systems and collaborate in person with Data Engineering, Infrastructure, AI Engineering, Security, and Business Leaders.
• Strong problem-solving, communication, and teamwork skills; comfortable working in a collaborative, on-site team environment.
• Foundational understanding of AI/ML concepts.

• Knowledge of GPU acceleration.

• 10%

The AI Platform Engineer is responsible for deploying and managing the computing and storage platform backbone that powers AI models. Working on-site alongside AI Data Engineering, IT Infrastructure, Security, and business stakeholders, this role is central to the reliability, performance, and scalability of our AI/ML infrastructure.

• Provision and maintain CPU and GPU compute clusters, high-bandwidth network equipment, and scalable, high-throughput storage systems for AI/ML training and inference, working on-site and virtually to coordinate hands-on configuration and troubleshooting with infrastructure teams.
• Develop and maintain scripts and infrastructure-as-code (IaC) modules for automated configuration of AI/ML infrastructure using tools such as Terraform and Ansible; participate in in-person and virtual code and design reviews with the platform engineering team.
• Develop and maintain CI/CD pipelines and container orchestration platforms (Kubernetes, Flux); troubleshoot pipeline failures and elevate to more senior engineers or other teams as needed, collaborating in person and virtually to resolve issues quickly and effectively.
• Set up and monitor data stores (file systems, block storage, traditional and vector databases) to support LLMs and RAG pipelines, with regular on-site coordination with AI Data Engineering teams to ensure data infrastructure meets evolving model requirements.
• Monitor system performance and log metrics to ensure reliability and uptime; implement system improvements under guidance of more senior engineers, including attending in-person and virtual planning and incident review sessions.
• Collaborate in person with AI Data Engineering, IT Infrastructure, Security, and Business Leaders to optimize infrastructure for performance and cost, including participating in cross-functional working sessions, architecture reviews, and stakeholder briefings.
• Deploy and maintain Model Context Protocol (MCP) connectors and ensure secure, scalable…