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Mid-senior metro role with broad cloud+ML skills at a well-known brand drives high competition.
Role requires specialized ML platform and cloud experience, making cross-industry transferability limited.
Explicit 5–8 years plus mandatory cloud, MLOps, Terraform, Kubernetes and CI/CD skills increases screening strictness.
Lead technical architecture and build scalable core machine learning platform services including training infrastructure, feature stores, and MLOps pipelines.
Drive design and implementation of cloud-native ML infrastructure, ensuring reliability, performance, automated model lifecycle management, and operational observability.
Collaborate cross-functionally with data science, engineering, and security teams to enable enterprise-wide responsible and compliant ML adoption.
5+ years relevant work experience in ML platform engineering or related Cloud/DevOps roles.
Proficiency with cloud platforms (AWS, Azure, or GCP) and infrastructure as code tools like Terraform.
Hands-on experience with CI/CD for ML, MLOps, containerization (Kubernetes/Docker), and machine learning infrastructure.
Bachelor’s degree or equivalent combination of education and experience.
Senior technologist comfortable with hands-on engineering and influencing ML platform strategy and architectural decisions.
Experienced in designing distributed, scalable systems for enterprise-scale machine learning deployments.
Skilled in integrating security, compliance, and responsible AI principles into ML platform operations and infrastructure.