Platform Engineer – AI Systems / Infrastructure IRC299414

GlobalLogic is a trusted digital engineering partner to the world’s largest companies, and they are seeking a Platform Engineer to enhance their AI Systems and Infrastructure. The role involves designing and building a core execution platform for algorithms in video game environments, utilizing cloud infrastructure and ensuring system reliability.

Responsibilities:

• 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

Requirements:

• 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