Data Engineer Senior Staff MLOps Engineer

About This Role

This senior staff role focuses on leading the design, development, and implementation of on-premises MLOps solutions to support the delivery of machine learning models. It involves collaborating with data scientists and software engineers to build scalable data pipelines and model training/deployment systems, and developing tools to ensure infrastructure reliability. The role suits an experienced MLOps professional who can mentor junior team members and stay at the forefront of the field.

Responsibilities

• Lead the design, development, and implementation of on-premises MLOps solutions that support the delivery of machine learning models
• Collaborate with data scientists and software engineers to build scalable and efficient data pipelines and model training and deployment systems
• Develop and maintain monitoring and management tools to ensure the reliability and performance of the on-premises MLOps infrastructure
• Work with stakeholders across the organization to understand their machine learning needs and requirements, and to develop MLOps solutions that meet those needs
• Stay up-to-date with the latest trends and technologies in MLOps, machine learning, and artificial intelligence, and share knowledge with the team
• Mentor junior members of the team and help them grow their skills and expertise in MLOps
• Troubleshoot and debug user issues

Requirements

• 5+ years of experience in MLOps, Software engineering, Data engineering, or a related field
• Strong programming skills in a modern programming language (Java, Scala, Python, Kotlin)
• Hands-on experience with Kubernetes—deploying and operating workloads (jobs, services, or ML/training pipelines), debugging pod failures, and working with core concepts (Deployments, ConfigMaps/Secrets, Services, resource limits)
• Excellent communication and collaboration skills, and the ability to work effectively in a team environment
• Value code simplicity and performance

Nice to have:

• Understanding of ML Projects lifecycle
• Experience with MLOps platforms, such as Kubeflow or MLFlow
• Experience with ML frameworks – like scikit-learn, LGBM, Tensorflow, PyTorch etc.
• Experience with Big Data tools – Spark, S3, Hadoop
• Strong knowledge of containerization and container orchestration technologies, such as Docker and Kubernetes
• Experience with DevOps and CI/CD practices
• Experience building and scaling model serving tools, i.e building APIs with tight SLAs
• Passion for the engineering behind machine learning, and scaling it
• Experience designing and building MLOps infrastructure, including data pipelines, model training and deployment systems, and monitoring and management tools