Senior Technical Architect for AI Training

Role Overview

Apply hands-on platform engineering expertise to help train next-generation AI systems through high-quality, real-world infrastructure scenarios. You will create reinforcement learning environments that assess an AI model''s ability to design, deploy, troubleshoot, secure, scale, and recover production-grade cloud infrastructure. Prior AI experience is not required, your production engineering expertise is the focus.

Key Responsibilities

• Create realistic cloud infrastructure tasks covering distributed systems, networking, security, scalability, and reliability.
• Develop scenarios involving IAM, queues, durable storage, observability, rolling deployments, and disaster recovery.
• Build reproducible, containerized environments with valid reference solutions and intentionally defective variants.
• Define measurable requirements for infrastructure configuration, deployed topology, and runtime behavior.
• Develop deterministic integration, load, security, failure-injection, deployment, and recovery tests.
• Debug environments, document technical decisions, and review tasks created by other experts.

Qualifications

• Senior-level experience in technical architecture, cloud infrastructure, platform engineering, DevOps, systems engineering, or SRE, including personal ownership of a production platform.
• Strong knowledge of distributed systems, scalable APIs, queues, autoscaling, durable storage, and partial-failure scenarios.
• Practical experience with IAM, private networking, least-privilege access, and service-to-service security.
• Experience with observability, measurable SLOs, rolling deployments, rollback strategies, and disaster recovery.
• Ability to write infrastructure automation or testing tools and debug containerized environments using a relevant programming language.

Preferred Qualifications

• Experience with Terraform or OpenTofu.
• Experience with AWS, Azure, GCP, Kubernetes, or multi-cloud infrastructure.
• Experience building internal developer platforms, edge infrastructure, or shared platform services.
• Experience with chaos engineering, fault injection, local cloud emulators, or resilience testing.
• Experience creating technical evaluations, automated grading systems, or AI environments is helpful but not required.

Work Terms

• Remote independent contractor engagement.
• Work is output-based and requires a minimum number of task submissions each week.
• Time required may vary based on your experience and workflow. Minimum submission requirements apply.

Compensation

• $60 to $130 per hour.
• Compensation is paid per task that meets project specifications.

Application Process

• Submit an application and complete the screening questions.
• Complete an approximately 30-minute AI interview, which is reviewed by recruiters.
• Selected candidates proceed to hiring manager review.