MLOps Engineer

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

Job summary

Bright Vision Technologies is seeking an accomplished MLOps Engineer for a 100% remote role in the U.S. You will design, build, and operate high-performance inference platforms for serving large machine learning models in production, focusing on latency, throughput, and reliability.

The ideal candidate has strong distributed systems experience, hands-on work with LLM inference frameworks, Kubernetes, and cloud platforms, plus a proven track record in observability, performance tuning, and

Qualifications

• Bachelor’s or Master’s degree in CS or a related field.
• 6+ years in distributed systems, infrastructure, or ML platform engineering.
• Strong Python and system language (Go, Rust, or C++) skills.
• Experience operating high-throughput, low-latency services in production.
• Hands-on with LLM or large-model inference frameworks (vLLM, TensorRT-LLM).
• Familiar with Kubernetes, autoscaling, and cloud platforms.
• Experience with observability stacks and performance engineering.
• 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, 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 including latency histograms, queue dynamics, GPU utilization, and error tracking.
• Develop deployment workflows including canary releases, shadow testing, and automated rollback.
• Operate incident response for high-availability AI services and drive durable reliability improvements.
• Collaborate with ML and product teams to support new model releases and capability rollouts.
• Implement security controls including request signing, content filtering, and abuse detection at the serving layer.
• Document operational procedures, performance characteristics, and tuning guidance for internal teams.
• Stay current with AI serving research and translate advances into production capabilities.

Skills

Distributed systems

Python

System languages (Go/Rust/C++)

Performance engineering

Observability

Incident response & communication

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

Kubernetes

LLM inference frameworks (vLLM, TensorRT-LLM)

Go

Rust

C++

Python

Job description

MLOps Engineer -Remote

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.

This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Job Title: MLOps Engineer

Location: 100% Remote (U.S.)

Position Type: Full-time, Direct W2

Salary Range: $100,000–$150,000 Annually

Experience Required: 6+ years

Sponsorship: U.S. Citizens, Green CardHolders, EADHolders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.

Job Summary

We are seeking aMLOps Engineer Engineerto design, build, andoperatehigh-performance,highly reliableinference platforms for serving large machine learning models in production. The role focuses on the systems engineering side of AI deployment, including request routing, batching, caching, autoscaling, GPUutilization, and end-to-end observability across diverse model workloads. The ideal candidate brings strong distributed systems and performance engineeringexpertise, has shipped serving systems at scale, and understands the trade-offs between latency, throughput, cost, and quality in ML serving.

Key Responsibilities

• Design andoperatemodel serving platforms supporting diverse workloads including LLMs, vision models, and recommendation systems.
• Optimizeinference 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 GPUutilization, 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 whereappropriate.
• Drive end-to-end observability including latency histograms, queue dynamics, GPUutilization, and error tracking.
• Develop deployment workflows including canary releases, shadow testing, and automated rollback.
• Operate incident response for high-availability AI services and drive durable reliability improvements.
• Collaborate with ML and product teams to support new model releases and capability rollouts.
• Implement security controls including request signing, content filtering, and abuse detection at the serving layer.
• Document operational procedures, performance characteristics, and tuning guidance for internal teams.
• Stay current with AI serving research andtranslateadvances into production capabilities.

Required Qualifications

• Bachelor’s orMaster’s degree in Computer Scienceor a related field.
• Six or more years of experience in distributed systems, infrastructure, or ML platform engineering.
• Strongproficiencyin Python anda systemslanguage such as 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 asvLLMorTensorRT-LLM.
• Strong understanding of GPU architecture, memory hierarchies, and acceleratorutilization.
• 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 communicationand incident response skills.

Preferred Qualifications

• 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.

Bright Vision Technologies is an Equal Opportunity Employer.

Equal Employment Opportunity (EEO) Statement

Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.

BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.