MLOps Engineer

🕒 July 9

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Dell Technologies

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1984

🔧 Hardware

🏢 Enterprise

🤝 B2B

🔥 Funding within the last year

💰 $4.5G Post-IPO Debt - Dell Technologies on 2025-09

Hardware • Enterprise • B2B

Dell Technologies is a multinational technology company that designs, manufactures, and sells a broad range of computing products and IT infrastructure. Dell is best known for PCs and laptops for consumers and businesses, as well as servers, storage, networking, and enterprise solutions including software and services. The provided text appears to be CSS/HTML fragments from Dell's website (masthead, shopping cart, icons), indicating an e-commerce and web presence tied to hardware and enterprise offerings.

📋 Description

• Design, implement, and manage robust MLOps pipelines for the deployment, monitoring, and maintenance of machine learning models in production environments. • Collaborate with cross-functional teams, including data scientists, software engineers, and DevOps professionals, to ensure efficient integration of machine learning models into existing systems and processes. • Continuously improve CI/CD processes to automate model training, evaluation, testing, and deployment. • Implement and maintain monitoring and observability solutions to track model performance, data quality, system reliability, and ML infrastructure. • Identify, troubleshoot, and resolve issues related to infrastructure, pipelines, and ML operations, staying up to date with MLOps trends, technologies, and best practices to contribute to the evolution of ML deployment strategies.

🎯 Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field; a postgraduate or advanced degree is a plus. • Solid experience in MLOps or related areas, including deploying and managing machine learning models using Docker, Kubernetes, and orchestration platforms such as Apache Airflow. • Strong programming skills in Python, experience with version control systems (Git), and a solid understanding of containerization, virtualization, and Infrastructure as Code (IaC) principles. • Experience with monitoring and logging tools such as Prometheus and the ELK Stack, along with excellent problem-solving skills and the ability to work in collaborative environments. • Fluent English for working in a global team. • Experience with machine learning frameworks such as TensorFlow and PyTorch, data processing libraries such as pandas and NumPy, and knowledge of best security practices for ML deployments. • Previous experience with CI/CD pipelines, automated testing for machine learning models, orchestration and model-management platforms like MLflow or Kubeflow, and familiarity with tools such as FinancialForce, EPN/PEF, Control Tower, OEP, GPT, DSA, FMPro, ServiceNow, and SSO, as well as services including Chromebook Enrollment, ReadyStock Order Life Cycle & Management, Connected Services (Shared Connected Configuration, Dedicated Connected Configuration, and Connected Provisioning), Logistics Services (Overpack, Multi-Pack, FTL/LTL, and Order Consolidation), and First Article/Change Management.

🏖️ Benefits

• What you'll achieve: • Design, implement, and manage robust MLOps pipelines for the deployment, monitoring, and maintenance of machine learning models in production environments. • Work closely with data scientists, software engineers, and DevOps teams to accelerate delivery of advanced generative AI solutions and drive the evolution of the organization's ML deployment strategies.