Machine Learning Engineer

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Job Summary:

Join our customer's team as a Machine Learning Engineer and help deliver end-to-end machine learning solutions using the latest cloud and container technologies. As part of a dynamic group, you’ll bridge data science innovation with scalable deployment, empowering impactful ML projects in a modern, hybrid work environment.

Key Responsibilities:

• Design, build, and maintain machine learning pipelines using CI/CD methodologies on Kubernetes.

• Deploy, manage, and optimize Kubeflow and other MLOps platforms to operationalize data science projects.

• Implement GitOps workflows with Argo to ensure automated, repeatable deployments.

• Develop cloud-native infrastructure as code (IaC) and manage environments within AWS.

• Collaborate closely with data scientists and engineers to transition models from research to production.

• Ensure robust security, monitoring, and debugging of deployed ML systems.

• Contribute to documentation and promote best practices in written and verbal communication within the team.

Required Skills and Qualifications:

• Strong experience with Kubernetes for building and operating scalable ML solutions.

• Deep understanding of CI/CD, with hands-on experience in cloud-native ML workloads.

• Proficiency in deploying and managing Kubeflow and/or AWS SageMaker.

• Solid background with AWS Cloud infrastructure and services.

• Expertise in Python and Bash scripting for automation and system integration.

• Familiarity with infrastructure as code (IaC) and GitOps tools such as Argo.

• Excellent written and verbal communication skills; ability to clearly convey technical concepts.

Preferred Qualifications:

• Prior experience with ML security and debugging best practices.

• Hands-on experience with end-to-end operationalization of data science projects on Kubernetes.

• Exposure to continuous testing (CT) in ML workflows.