Remote Data Platform Engineer for ML Serving & Scale

Bright Vision Technologies; Tampa (FL); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking a Data Platform Engineer to design, build, and operate high-performance inference platforms for serving large ML models in production. The role emphasizes systems engineering for AI deployment, including routing, batching, caching, autoscaling, GPU utilization, and observability across model workloads.

The ideal candidate has strong distributed systems and performance engineering experience, has shipped serving systems at scale, and understands latency,

Qualifications

• Bachelor’s or Master’s degree in Computer Science or related field.
• 6+ years of experience in distributed systems, infrastructure, or ML platform engineering.
• Strong proficiency in Python and a systems language (Go/Rust/C++).
• Deep experience operating high-throughput, low-latency services in production.
• Hands-on experience with LLM/large model inference frameworks such as vLLM or TensorRT-LLM.
• Familiarity with Kubernetes, autoscaling, and modern cloud platforms.
• Experience with observability stacks including metrics, tracing, and structured logging.
• Strong communication and incident response skills.

Responsibilities

• Design, build, and operate high-performance inference platforms for serving large ML models in production.
• Focus on systems engineering for AI deployment: routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability.
• Understand trade-offs between latency, throughput, cost, and quality in ML serving.

Skills

Python

Go

Rust

C++

Distributed systems

Performance engineering

Observability

Incident response

Communication

Education

Bachelor’s or Master’s in CS

Tools

vLLM

TensorRT-LLM

Job description

Bright Vision Technologies is seeking a Data Platform Engineer to design, build, and operate high-performance inference platforms for serving large ML models in production. The role emphasizes systems engineering for AI deployment, including routing, batching, caching, autoscaling, GPU utilization, and observability across model workloads.

The ideal candidate has strong distributed systems and performance engineering experience, has shipped serving systems at scale, and understands latency,