Top Gen AI Jobs Hiring! Full Time AI Infrastructure Engineer in - Ricebowl

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AI Infrastructure Engineer

Pokee AI

Singapore

3+ years

Today

$130K–150K/yr

Full-time

Remote

Skills Required

vLLM

TensorRT-LLM

Python

Rust

C++

Go

TensorRT

Triton

ONNX Runtime

Kubernetes

Docker

AWS

GCP

GPU computing

distributed systems

Description

Pokee AI is hiring an AI Infrastructure Engineer to build and optimize the systems behind RL-trained AI agents. The role focuses on scalable training, high-performance inference serving, and production infrastructure for enterprise use.

Company: Pokee AI

Role: AI Infrastructure Engineer

Location: Remote (US/Singapore Preferred)

Experience

• 3+ years of experience in ML infrastructure, ML platform engineering, or a related systems role
• Strong proficiency in Python and systems-level languages such as Rust, C++, or Go
• Hands-on experience with ML serving frameworks such as vLLM, TensorRT, Triton, or ONNX Runtime
• Experience with container orchestration and cloud infrastructure such as Kubernetes, Docker, AWS, or GCP
• Solid understanding of GPU computing, distributed systems, and performance profiling
• Familiarity with ML experiment tracking and pipeline orchestration tools such as MLflow, Weights & Biases, or Airflow
  
  

Responsibilities

• Build and optimize scalable training and inference infrastructure for RL-based AI agent models
• Optimize model serving for latency, throughput, and cost across cloud and on-device deployments
• Develop and manage CI/CD pipelines, experiment tracking, and model versioning systems
• Implement 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
  
  

Additional Responsibilities

• Support cloud, on-premise, and on-device deployments
• Align infrastructure with enterprise reliability, security, and compliance needs
  
  

Nice To Have

• Experience with on-device or edge inference optimization such as GGUF quantization, TensorRT-LLM, CoreML, or QNN
• Familiarity with on-premise GPU deployments such as NVIDIA DGX, Dell PowerEdge, or 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
  
  

More Skills

performance profiling, MLflow, Weights & Biases, Airflow, CI/CD pipelines, experiment tracking, model versioning, data pipelines, reward signal computation, RL-based AI agent models, SOC 2, data residency

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