Platform Engineer - AI Systems / Infrastructure - Remote / Telecomuute

• Design and build the core execution / runner engine for game and algorithm execution.
• Architect and implement distributed platform components across runner, renderer, application, and supporting services.
• Build scalable cloud-native infrastructure using AWS services such as compute, storage, queues, IAM, ECS/EKS/Lambda/Batch, and S3.
• Implement Infrastructure as Code using Terraform.
• Develop asynchronous job processing capabilities using queues, workers, retries, and fault-tolerant execution patterns.
• Ensure environment parity between local development and cloud/private infrastructure execution.
• Build and maintain CI/CD pipelines for automated testing, deployment, and long-running workflow execution.
• Design secure and reproducible external code ingestion workflows, including git-based pipelines, SHA pinning, versioning, and controlled execution of externally developed algorithms.
• Design data persistence layers for logs, artifacts, run outputs, and execution metadata.
• Implement reliability patterns including idempotency, concurrency handling, failure recovery, retries, and observability hooks.
• Operate within private, restricted, or air-gapped execution environments as required.
• Collaborate closely with Full Stack and AI Evaluation engineers on APIs, schemas, storage formats, execution lifecycle, and reporting needs.
• Ensure all source code, configuration, tests, and documentation comply with client-defined standards, security policies, and review processes.

• Advanced Python backend engineering experience.
• Strong distributed systems and asynchronous processing experience.
• Hands-on AWS experience, especially with compute, storage, IAM, queues, containers, and batch-style workloads.
• Terraform / Infrastructure as Code experience.
• Docker and containerized workload execution experience.
• CI/CD experience, preferably with GitHub Actions or equivalent.
• Strong understanding of queues, workers, orchestration, retries, idempotency, and failure handling.
• Experience designing storage systems for logs, artifacts, execution outputs, and metadata.
• Strong Git/versioning knowledge, including branching, commit hashes, reproducibility, and controlled code execution.
• Strong system-level design thinking across multi-service architectures.
• Ability to work inside client-owned repositories, infrastructure, workflows, and security controls., * Experience building AI/ML execution platforms, experimentation platforms, benchmark systems, or model evaluation infrastructure.
• Experience with reinforcement learning, LLM agents, or AI evaluation workflows.
• Experience operating in private network, restricted, or air-gapped environments.
• Experience with NoSQL, object storage, metadata catalogs, or S3-backed persistence.
• Experience with Kubernetes, ECS, AWS Batch, or large-scale distributed workload execution.
• Experience with security controls for third-party or externally developed code execution.

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