MLOps工程师
MLOps Engineer
MLOps 工程师 - 远程办公
Bright Vision Technologies 是一家技术咨询和软件开发公司，为美国各地提供云、AI、数据和企业解决方案。
这是加入一家知名且受人尊敬的组织的绝佳机会，提供巨大的职业发展潜力。
职位名称：MLOps 工程师
工作地点：100% 远程（美国）
职位类型：全职，直接 W2
薪资范围：每年 10 万至 15 万美元
所需经验：6 年以上
赞助：美国公民、绿卡持有者、EAD 持有者以及 H-1B 转移候选人欢迎申请。我们无法为该职位的新 H-1B 签证申请提供赞助。
职位概述
我们正在寻找一名 MLOps 工程师，负责设计、构建和运营高性能、高可靠性的推理平台，用于在生产环境中部署大型机器学习模型。该职位侧重于 AI 部署的系统工程方面，包括请求路由、批处理、缓存、自动扩展、GPU 利用率以及跨多种模型工作负载的端到端可观测性。理想的候选人具备强大的分布式系统和性能工程专业知识，有大规模部署系统的经验，并了解 ML 推理中延迟、吞吐量、成本和质量之间的权衡。
主要职责
· 设计和运营支持多种工作负载（包括 LLM、视觉模型和推荐系统）的模型推理平台。
- 使用连续批处理、分页注意力、推测解码和请求多路复用优化推理性能。
- 在模型端点上实现多租户路由、速率限制和服务质量策略。
- 构建平衡延迟、吞吐量和成本的自动扩展和容量管理系统。
- 优化 LLM 推理工作负载的 GPU 利用率、内存管理和 KV 缓存策略。
- 将模型推理集成到 API 网关、身份系统和可观测性平台中。
- 在适当的情况下实施缓存、提示去重和响应复用策略。
- 实现端到端的可观测性，包括延迟直方图、队列动态、GPU 利用率和错误跟踪。
- 开发部署流程，包括小范围发布、影子测试和自动化回滚。
- 运营高可用性 AI 服务的事件响应并推动持久的可靠性改进。
- 与 ML 和产品团队合作，支持新模型发布和功能上线。
查看英文原文
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 Card Holders, EAD Holders, 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 a MLOps Engineer Engineer to design, build, and operate high-performance, highly reliable inference 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, GPU utilization, and end-to-end observability across diverse model workloads. The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands the trade-offs between latency, throughput, cost, and quality in ML serving.
Key Responsibilities
· Design and operate model serving platforms supporting diverse workloads including LLMs, vision models, and recommendation systems.
- 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 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.
Required Qualifications
· 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 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.
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.
How to Apply
Would you like to know more about this opportunity? For immediate consideration, please send your resume to or contact us at (908) 505-3544. Learn more about Bright Vision Technologies at .
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.
Originally posted on Himalayas