Home/Jobs/AI Infrastructure Engineer

AI Infrastructure Engineer

Pokee AI

Singapore

3+ years

Today

$130K–150K/yr

Full-time

Remote

Skills Required

vLLM

TensorRT-LLM

Python

Rust

C++

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

Prepare for this role

Recommended resources to build the skills for this position. Sponsored.

AI Infrastructure Engineer

Stack mentioned

More jobs at Top Gen AI Jobs