Senior MLOps Engineer
角色具体信息职位描述作为高级 MLOps 工程师，你将专注于支持跨功能团队设计，部署和运营机器学习解决方案，同时在整个机器学习工程 (MLE) 生态系统中建立可扩展基础设施，工具和最佳实践。你将会做什么 · 在整个 ML 生命周期内与数据科学家和工程师合作，包括构建和扩大 ETL 管道，部署模型用于面向客户的应用程序，并通过云基础设施和工具实现高效模型开发 · 设计，构建和维护可扩展的机器学习基础设施，包括模型服务 （实时和批量）,培训环境和配套系统，以重点关注性能，可扩展性和成本效益。· 贡献机器学习工程和数据科学工具的路线图，包括开发可复用框架和标准化解决方案来简化模型实施。· 与数据科学家合作并支持通过实现云计算工具和基础设施的有效使用，并在 ML 生命周期中提供技术专业知识。·与机器学习工程师合作，分享知识，改善最佳实践，培养持续学习和发展的文化 ·支持开发并维护监测，警报和自动化测试框架以确保数据管道，模型和基础设施的可靠性，性能和完整性 ·在整个数据科学生命周期内制定，记录和传达实施和最佳实践 ·管理和向项目利益相关者沟通云基础设施成本和预算 ·保持 GCP 服务和机器学习技术和 MLOps 中不断变化的最佳实践的最新情况 ·可能会分配额外任务 需要 · 在 MLOps 或 DevOps 实践中经验，包括使用 Docker,Kubernetes,CI/CD 管道，基于 Git 的版本控制，API 开发，模型服务 （批量和实时）和自动测试框架的生产 ML 系统构建和运行；• 数据科学，计算机科学，统计学，应用数学或相等数量的领域的学士学位；•与数据科学家合作，在生产环境中部署，扩展和运营机器学习模型；3年以上作为一个具有成功记录的机器学习工程师经验3年以上
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Role Specific Information
Job Description
About the Role
As Senior MLOps Engineer, you will focus on supporting cross-functional teams in designing, deploying, and operating machine learning solutions while building scalable infrastructure, tools, and best practices across the Machine Learning Engineering (MLE) ecosystem.
What You’ll Do
- Collaborate with Data Scientists and Engineers across the full ML lifecycle, including building and scaling ETL pipelines, deploying models into customer-facing applications, and enabling efficient model development through cloud infrastructure and tooling
- Design, build, and maintain scalable machine learning infrastructure, including model serving (real-time and batch), training environments, and orchestration systems, with a focus on performance, scalability, and cost efficiency
- Contribute to the roadmap for Machine Learning Engineering and Data Science tools, including developing reusable frameworks and standardized solutions to streamline model implementation
- Partner with and support Data Scientists by enabling effective use of cloud-based tools and infrastructure, and providing technical expertise across the ML lifecycle
- Collaborate with machine learning engineers to share knowledge, improve best practices, and foster a culture of continuous learning and development
- Support development and maintain monitoring, alerting, and automated testing frameworks to ensure the reliability, performance, and integrity of data pipelines, models, and infrastructure
- Develop, document, and communicate implementations and best practices across the data science lifecycle
- Manage and communicate cloud infrastructure costs and budgets to project stakeholders
- Stay current with GCP services and evolving best practices in Machine Learning Engineering and MLOps
- Additional tasks may be assigned
What Skills You Have
Required
- Experience in MLOps or DevOps practices, including building and operating production ML systems using Docker, Kubernetes, CI/CD pipelines, Git-based version control, API development, model serving (batch and real-time), and automated testing frameworks
- Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field
- Experience working with Data Scientists to deploy, scale, and operationalize machine learning models in production environments
- 3+ years of experience as a Machine Learning Engineer with a proven track record of successful project delivery
- In-depth knowledge of cloud platform, preferably Google Cloud Platform services, particularly Vertex AI, BigQuery and Dataproc.
- Extensive expertise with CI/CD and
- IaC best practices
- Extensive knowledge of distributed computing and big data technologies like Spark, Kubeflow, Airflow and SQL
- Extensive expertise in Python and machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn)
- Experience working in Agile environments with an emphasis on iterative development and continuous delivery
Preferred
- Master’s Degree
- Proficiency in Java or other languages
- Retail experience
- E-commerce experience
- 5+ years of experience in Machine Learning
- Experience with optimization techniques and tools (e.g., Gurobi, linear programming, mixed-integer programming)
- Experience working with agent based or agentic AI systems, including orchestration of autonomous workflows or LLM-driven agents
Essential Functions
The requirements listed below are representative of functions you will be required to perform, however you may be required to perform additional functions. Kohl’s may revise this job description from time to time. To perform this job successfully, you must be able to perform each essential function satisfactorily. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions, absent undue hardship.
- Ability to perform the accountabilities listed in the “What You’ll Do” Section
- Ability to maintain prompt and regular attendance as set by the company
- Ability to work at least 8 hours per day, occasionally longer when necessary to meet business needs, 5 days per week
- Ability to comply with dress code requirements
- Ability to learn and comply with all company policies, procedures, standards and guidelines
- Ability to give direction and receive, understand and proactively respond to direction from leadership and other company personnel
- Ability to work as part of a team and interact effectively and appropriately with others
- Ability to maintain composure and work in a fast paced environment while accomplishing multiple tasks within established timeframes
- Ability to satisfactorily complete company training programs
- Perform work in accordance with the Physical/Cognitive Requirements section
Physical/Cognitive Requirements
- Ability to use a personal computer for tasks such as communicating, preparing reports, etc.
- Ability to plan, prioritize and monitor activities across business units
- Ability to complete or oversee the completion of assigned projects in a timely manner
- Ability to comply with health and safety standards
Originally posted on Himalayas