Remote AI Platform Engineer: Scalable LLM Inference

Bright Vision Technologies; Fishers (IN); Remote; USD 100,000 - 180,000; 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 ideal candidate will excel in distributed systems, LLM serving, GPU optimization, autoscaling, and cloud-native infrastructure, with a strong focus on performance, reliability, and observability.

The role requires 10+ years of experience in distributed systems, cloud infrastructure, and AI platform engineering, plus expertise in Kubernetes,

Qualifications

• 6+ years of experience in distributed systems, infrastructure, or ML platform engineering.
• Strong proficiency in Python and at least one systems language (Go, Rust, or C++).
• Experience with LLM inference frameworks (vLLM, TensorRT-LLM), Kubernetes, cloud platforms, and GPU optimization.

Responsibilities

• Design, build, and maintain scalable AI inference and model-serving platforms for production environments.
• Architect cloud-native infrastructure supporting LLMs and foundation models.
• Optimize inference latency, GPU utilization, memory management, and request scheduling across distributed AI workloads.
• Develop autoscaling, workload orchestration, traffic management, and routing strategies for AI services.
• Implement MLOps practices for model deployment, versioning, rollback, and lifecycle management.
• Build monitoring, observability, logging, tracing, and alerting for platform reliability.

Skills

Distributed systems

ML platform engineering

Python

Go

Rust

C++

LLM serving

Kubernetes

Cloud platforms

GPU optimization

Terraform / IaC

MLOps

CI/CD automation

Service mesh

GitOps

Education

Bachelor's or Master's degree in Computer Science or related field

Tools

vLLM

TensorRT-LLM

Triton Inference Server

Ray Serve

Docker

Kubernetes

AWS

Azure

GCP

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 ideal candidate will excel in distributed systems, LLM serving, GPU optimization, autoscaling, and cloud-native infrastructure, with a strong focus on performance, reliability, and observability.

The role requires 10+ years of experience in distributed systems, cloud infrastructure, and AI platform engineering, plus expertise in Kubernetes,