Remote MLOps Engineer: Scale AI Serving Platforms

Bright Vision Technologies; Sugar Land (TX); A distancia; USD 100.000 - 150.000; Jornada completa

Descripción de la vacante

Bright Vision Technologies is seeking an experienced MLOps Engineer to design, build, and operate high-performance inference platforms for serving large ML models in production. The role concentrates on systems engineering for AI deployment—routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse workloads.

The ideal candidate has strong distributed systems and performance engineering expertise, successfully shipped serving systems at scale, and

Formación

• Bachelor’s or Master’s degree in Computer Science or a related field.
• 6+ years of experience in distributed systems or ML platforms.
• Proficient in Python and at least one systems language (Go, Rust, or C++).
• Experience with LLM inference frameworks such as vLLM or TensorRT-LLM.
• Strong knowledge of GPU architectures and memory hierarchies.

Responsabilidades

• Design and operate model serving platforms supporting diverse workloads (LLMs, vision, recommendations).
• Optimize inference performance with batching, speculative decoding, and request multiplexing.
• Implement multi-tenant routing and QoS across model endpoints.
• Build autoscaling and capacity management balancing latency, throughput, and cost.
• Collaborate with ML and product teams to support model releases and capabilities.

Conocimientos

Distributed systems

Performance engineering

Observability

Incident response

Communication

Educación

Bachelor's or Master's in Computer Science

Herramientas

Python

Go

Rust

C++

Kubernetes

TensorRT-LLM

Descripción del empleo

Bright Vision Technologies is seeking an experienced MLOps Engineer to design, build, and operate high-performance inference platforms for serving large ML models in production. The role concentrates on systems engineering for AI deployment—routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse workloads.

The ideal candidate has strong distributed systems and performance engineering expertise, successfully shipped serving systems at scale, and