Remote MLOps Engineer - Build Scalable AI Serving

Bright Vision Technologies; United States; Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an MLOps Engineer to design and operate high-performance inference platforms for large ML models in production. You will focus on distributed systems, latency, throughput, and observability across diverse workloads, collaborating with ML and product teams to roll out new models.

The role emphasizes systems engineering for AI deployment, including batching, caching, autoscaling, and security at the serving layer, with a strong emphasis on reliability and cost

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, or C++).
• Deep experience operating 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, paged attention, speculative decoding, and request multiplexing.
• 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, memory management, and KV cache strategies for LLM serving workloads.
• Integrate model serving with API gateways, identity systems, and observability platforms.
• Implement caching, prompt deduplication, and response reuse strategies.
• Drive end-to-end observability including latency histograms and error tracking.
• Develop deployment workflows including canary releases, shadow testing, and automated rollback.
• Operate incident response for high-availability AI services and drive reliability improvements.
• Collaborate with ML and product teams to support new model releases and capability rollouts.
• Implement security controls including request signing and abuse detection at the serving layer.
• Document operational procedures and performance characteristics for internal teams.
• Stay current with AI serving research and translate advances into production capabilities.

Skills

Python

Go

Rust

C++

Kubernetes

observability

performance engineering

Education

Bachelor’s or Master’s degree in Computer Science

Tools

vLLM

TensorRT-LLM

Job description

Bright Vision Technologies is seeking an MLOps Engineer to design and operate high-performance inference platforms for large ML models in production. You will focus on distributed systems, latency, throughput, and observability across diverse workloads, collaborating with ML and product teams to roll out new models.

The role emphasizes systems engineering for AI deployment, including batching, caching, autoscaling, and security at the serving layer, with a strong emphasis on reliability and cost