MLOps Engineer

Bright Vision Technologies•Highlands Ranch, CO

3d•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is seeking an MLOps Engineer to design, build, and operate high-performance, reliable inference platforms for production machine learning models. The position focuses on systems engineering for AI deployment, including request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse model workloads. The ideal candidate has distributed systems and performance engineering expertise, experience shipping serving systems at scale, and an understanding of latency, throughput, cost, and quality trade-offs in ML serving. This is a full-time, direct W2 position with Bright Vision Technologies, working remotely from the continental United States on the company’s in-house Statement of Work engagement. The company does not engage in C2C, 1099, or third-party arrangements for this role. New H1B sponsorship is unavailable, but qualified candidates currently on a valid H1B visa may apply for a transfer. A technical coding assessment is mandatory.

Requirements

Bachelor’s or master’s degree in computer science or a 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++.
Deep experience operating high-throughput, low-latency production services.
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.
Must be willing to work directly as a full-time W2 employee and contribute to the company’s in-house SOW deliverables.
Must complete a technical coding assessment.
No new H1B sponsorship is available; candidates with a valid H1B visa who need a transfer may apply.

Nice To Haves

Open-source contributions to model-serving infrastructure.
Experience with multi-region or globally distributed AI serving.
Familiarity with model quantization, distillation, and compression techniques.
Exposure to FinOps for AI workloads and cost-efficient serving design.
Experience supporting external-facing AI APIs at scale.

Responsibilities

Design and operate model-serving platforms for LLM, vision, and recommendation-system workloads.
Optimize inference performance using continuous batching, paged attention, speculative decoding, and request multiplexing.
Implement multi-tenant routing, rate limiting, and quality-of-service policies across model endpoints.
Build autoscaling and capacity-management systems that balance 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 where appropriate.
Drive end-to-end observability for latency histograms, queue dynamics, GPU utilization, and errors.
Develop deployment workflows, including canary releases, shadow testing, and automated rollback.
Operate incident response for highly available AI services and drive durable reliability improvements.
Collaborate with ML and product teams on model releases and capability rollouts.
Implement serving-layer security controls, including request signing, content filtering, and abuse detection.
Document operational procedures, performance characteristics, and tuning guidance for internal teams.
Stay current with AI-serving research and translate advances into production capabilities.