2+ years building or operating Reinforcement Learning (RL) post-training pipelines for Large Language Models (LLMs) and agentic models using verl, slime, MILES, or comparable frameworks, with a demonstrated understanding of the end-to-end training stack, including pretraining and post-training frameworks, Reinforcement Learning Environments (RLEs), rollout inference engines (for example, vLLM or SGLang), evaluation, and checkpointing, and how design choices at each layer affect throughput, stability, and cost. 5+ years designing, building, and operating large-scale distributed systems or cloud platforms with end-to-end ownership of production services, including Kubernetes-based containerized platforms on Microsoft Azure, Amazon Web Services (AWS), or Google Cloud, control-plane or backend services that manage compute, storage, and networking, and secure multi-tenant designs covering authentication, authorization, workload identity, quota management, and isolation.