Senior MLOps Engineer

Salary: $126,000 - 166,000 per year Requirements:

• We require experience in MLOps or DevOps practices, including operating production machine learning systems with Docker, Kubernetes, CI/CD pipelines, Git-based version control, API development, batch and real-time model serving, and automated testing.
• We require a bachelors degree in Data Science, Computer Science, Statistics, Applied Mathematics, or another quantitative discipline, or an equivalent background.
• We require experience partnering with Data Scientists to deploy, scale, and operationalize machine learning models in production.
• We require at least 3 years of experience as a Machine Learning Engineer with a strong record of delivering projects successfully.
• We require solid knowledge of cloud platforms, preferably Google Cloud Platform, especially Vertex AI, BigQuery, and Dataproc.
• We require advanced experience with CI/CD and Infrastructure as Code best practices.
• We require strong knowledge of distributed computing and big data technologies such as Spark, Kubeflow, Airflow, and SQL.
• We require advanced Python skills and familiarity with machine learning libraries such as TensorFlow, PyTorch, and scikit-learn.
• We require experience working in Agile environments with an emphasis on iterative delivery and continuous release cycles.
• Preferred qualifications include a masters degree, Java or other programming language proficiency, retail or e-commerce experience, 5+ years in machine learning, optimization experience, and exposure to agent-based or agentic AI systems.

• We collaborate with Data Scientists and Engineers across the full machine learning lifecycle, including building and scaling ETL pipelines, deploying models into customer-facing applications, and enabling efficient model development through cloud infrastructure and tooling.
• We design, build, and maintain scalable machine learning infrastructure, including real-time and batch model serving, training environments, and orchestration systems, with attention to performance, scalability, and cost effectiveness.
• We contribute to the roadmap for Machine Learning Engineering and Data Science tools by developing reusable frameworks and standardized solutions that simplify model implementation.
• We support Data Scientists by enabling effective use of cloud-based tools and infrastructure and by providing technical expertise throughout the machine learning lifecycle.
• We partner with machine learning engineers to share knowledge, improve best practices, and strengthen a culture of continuous learning and development.
• We develop and maintain monitoring, alerting, and automated testing frameworks to protect the reliability, performance, and integrity of data pipelines, models, and infrastructure.
• We document and communicate implementations and best practices across the data science lifecycle.
• We manage and communicate cloud infrastructure costs and budgets to project stakeholders.
• We stay current with Google Cloud Platform services and evolving best practices in Machine Learning Engineering and MLOps.
• We may also take on additional tasks as needed.

• Agentic AI
• AI
• Airflow
• API
• Big Data
• BigQuery
• CI/CD
• Cloud
• DevOps
• Docker
• ETL
• Git
• Support
• Java
• Kubeflow
• Kubernetes
• Machine Learning
• MLOps
• Model Serving
• PyTorch
• Python
• SQL
• Spark
• TensorFlow
• LLM

More:

We are hiring a Senior MLOps Engineer to support cross-functional teams in designing, deploying, and operating machine learning solutions while building scalable infrastructure, tools, and best practices across our Machine Learning Engineering ecosystem. This role is part of Kohls, and we value collaboration, continuous improvement, and practical technical impact. The position also includes expectations around teamwork, regular attendance, adherence to company standards, and working in a fast-paced environment while managing multiple priorities.

last updated 33 week of 2026