Remote MLOps Engineer — Scalable AI Inference

Bright Vision Technologies; Maple Grove (MN); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an MLOps Engineer to design, build, and operate high-performance inference platforms for large ML models in production. This role emphasizes systems engineering, request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse workloads.

The ideal candidate has strong distributed systems experience, shipped serving systems at scale, and understands latency, throughput, and cost trade-offs in ML serving.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or related field.
• Six or more years of experience in distributed systems, infrastructure, or ML platform engineering.
• Strong proficiency in Python and a systems language such as Go, Rust, or C++.
• Experience with high-throughput, low-latency services in production.
• Hands-on experience with LLM or large model inference frameworks such as vLLM or TensorRT-LLM.
• Strong understanding of GPU architecture, memory hierarchies, and accelerator utilization.
• Familiarity with Kubernetes, autoscaling, and modern cloud platforms.
• Experience with observability stacks including metrics, tracing, and structured logging.
• Solid grounding in performance engineering and capacity planning.
• Strong communication and incident response skills.

Responsibilities

• Design and operate model serving platforms supporting diverse workloads including LLMs, vision models, and recommendation systems.
• Optimize inference performance using batching, paging, and caching strategies.
• Implement multi-tenant routing, rate limiting, and QoS policies across model endpoints.
• Build autoscaling and capacity management systems balancing latency, throughput, and cost.
• Tune GPU utilization and memory management for serving workloads.
• Integrate model serving with API gateways, identity systems, and observability platforms.
• Develop caching and response reuse strategies where appropriate.
• Drive end-to-end observability including latency histograms and queue dynamics.
• Develop deployment workflows with canary releases, shadow testing, and rollback.
• Operate incident response for high-availability AI services and drive reliability improvements.
• Collaborate with ML and product teams on new model releases and rollouts.
• Implement security controls at the serving layer and document procedures.

Skills

Python

Distributed systems

Performance engineering

Incident response

ML model serving

Communication

System monitoring

Education

Bachelor’s or Master’s in CS or related

Tools

Kubernetes

TensorRT/LLM frameworks

vLLM

Go/Rust/C++

CUDA/GPU architecture

Job description

Bright Vision Technologies is seeking an MLOps Engineer to design, build, and operate high-performance inference platforms for large ML models in production. This role emphasizes systems engineering, request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse workloads.

The ideal candidate has strong distributed systems experience, shipped serving systems at scale, and understands latency, throughput, and cost trade-offs in ML serving.