Senior AI Platform Engineer - Remote Cloud-Native ML Infra

Bright Vision Technologies; Columbus, Dublin (OH, OH); On-site; USD 130,000 - 180,000; Full time

Job summary

Bright Vision Technologies seeks an AI Platform Engineer to design, build, and operate enterprise-scale AI inference platforms. You will drive cloud-native infrastructure, distributed systems, and ML workflows to support production workloads.

The role emphasizes LLM serving, GPU optimization, Kubernetes, and strong collaboration with ML teams to deliver reliable, cost-efficient AI services.

Qualifications

• 10+ years of professional experience in distributed systems, infrastructure engineering, cloud platforms, or ML platform engineering.
• Strong programming skills in Python and at least one systems programming language such as Go, Rust, or C++.
• Extensive experience with Large Language Model (LLM) serving, model inference optimization, and production AI infrastructure.

Responsibilities

• Design, build, and maintain scalable AI inference and model-serving platforms for enterprise production environments.
• Architect cloud-native infrastructure supporting LLMs, foundation models, and ML services.
• Optimize inference latency, GPU utilization, memory management, and request scheduling across distributed AI workloads.
• Develop monitoring, observability, logging, and alerting to ensure platform reliability and performance.
• Collaborate with AI researchers, ML engineers, DevOps teams, and software engineers to deploy and support production AI models.

Skills

Distributed systems

Python

Go

Rust

C++

LLM serving frameworks

Kubernetes

Cloud platforms

GPU optimization

Education

Bachelor's or Master's degree in Computer Science / Computer Engineering / AI

Tools

vLLM

TensorRT-LLM

Trition Inference Server

Ray Serve

Job description

Bright Vision Technologies seeks an AI Platform Engineer to design, build, and operate enterprise-scale AI inference platforms. You will drive cloud-native infrastructure, distributed systems, and ML workflows to support production workloads.

The role emphasizes LLM serving, GPU optimization, Kubernetes, and strong collaboration with ML teams to deliver reliable, cost-efficient AI services.