Remote AI Platform Engineer for Scalable ML Infra

Bright Vision Technologies; Powell (OH); Remote; USD 140,000 - 210,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Platform Engineer to design, build, and operate scalable AI inference platforms for production ML workloads. The role emphasizes distributed systems, LLM serving, GPU optimization, and cloud-native infrastructure to deliver reliable, cost-efficient AI platforms.

The ideal candidate has 10+ years of experience in distributed systems and AI platform engineering, with deep expertise in Kubernetes, cloud infrastructure, and MLOps.

Qualifications

• Requires 6+ years in distributed systems, infra, or ML platform eng.
• Proficient in Python and at least one systems language (Go/Rust/C++).
• Extensive experience with LLM serving and production AI infra.

Responsibilities

• Design and maintain scalable AI model serving platforms.
• Optimize inference performance and GPU utilization.
• Build autoscaling, deployment, and monitoring solutions.
• Collaborate with ML teams to deploy production AI models.
• Architect cloud-native infrastructure for LLMs and ML services.
• Develop observability, tracing, and alerting for reliability.
• Mentor teams and promote AI platform engineering best practices.

Skills

Distributed systems

Python

Go

Rust

C++

Kubernetes

Cloud platforms

GPU optimization

LLM serving

MLOps

Observability

FinOps

Multi-region deployments

Education

Bachelor's or Master's in CS/CE/AI

Tools

vLLM

TensorRT-LLM

Triton Inference Server

Ray Serve

Docker

Kubernetes

Job description

Bright Vision Technologies is seeking an AI Platform Engineer to design, build, and operate scalable AI inference platforms for production ML workloads. The role emphasizes distributed systems, LLM serving, GPU optimization, and cloud-native infrastructure to deliver reliable, cost-efficient AI platforms.

The ideal candidate has 10+ years of experience in distributed systems and AI platform engineering, with deep expertise in Kubernetes, cloud infrastructure, and MLOps.