[Remote] AI Infrastructure Engineer

Note: The job is a remote job and is open to candidates in USA. Pokee AI is building AI agents trained through reinforcement learning for enterprise use. The AI Infrastructure Engineer will build and optimize scalable training pipelines, high-performance inference serving, data pipelines, and production infrastructure across cloud, on-premise, and on-device environments. The role also involves collaborating with research scientists and ensuring infrastructure reliability, security, and compliance.

Responsibilities

• Design, build, and maintain scalable training and inference infrastructure for RL-based AI agent models
• Optimize model serving for latency, throughput, and cost across cloud (AWS, GCP) and on-premise/on-device environments
• Develop and manage CI/CD pipelines, experiment tracking, and model versioning systems
• Implement efficient data pipelines for training data collection, preprocessing, and reward signal computation
• Collaborate with research scientists to productionize new algorithms and model architectures
• Ensure infrastructure meets enterprise requirements for reliability, security, and compliance (SOC 2, data residency)

Skills

• • 3+ years of experience in ML infrastructure, ML platform engineering, or a related systems role
• • Strong proficiency in Python and systems-level languages (Rust, C++, or Go)
• • Hands-on experience with ML serving frameworks (vLLM, TensorRT, Triton, ONNX Runtime, or similar)
• • Experience with container orchestration (Kubernetes, Docker) and cloud infrastructure (AWS or GCP)
• • Solid understanding of GPU computing, distributed systems, and performance profiling
• • Familiarity with ML experiment tracking and pipeline orchestration tools (MLflow, Weights & Biases, Airflow, or similar)
• • Experience with on-device / edge inference optimization (GGUF quantization, TensorRT-LLM, CoreML, QNN)
• • Familiarity with on-premise GPU deployments (NVIDIA DGX, Dell PowerEdge, Lenovo ThinkStation)
• • Experience supporting RL training loops or online learning systems in production
• • Background in enterprise software with knowledge of security and compliance frameworks
• • Contributions to open-source ML infrastructure projects

Benefits

• Remote work in the United States or Singapore
• Direct impact on the product
• Access to cutting-edge research
• The opportunity to shape the future of enterprise AI from the ground up

Company Overview

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