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Mid-level ML title, 3-4 years experience, hybrid metro role at a known global employer increases applicant competition.
MLOps skills are transferable across industries but require ML deployment experience, so moderate sensitivity.
Explicit 3-4 year requirement and extensive MLOps/cloud/tooling stack make filters strict.
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Deploy and manage core AI and data science models within a global mobility platform, ensuring production readiness and scale.
Develop and maintain MLOps infrastructure, including model versioning, deployment pipelines, monitoring, and observability for system reliability.
Collaborate cross-functionally with data scientists, cloud engineers, and data engineers to optimize AI model productionalization and integrate cloud-native and containerized environments.
3-4 years of experience in deploying, monitoring, and maintaining machine learning models in cloud environments.
Bachelor's degree in Computer Science required; Master's degree preferred.
Proficiency in cloud platforms (AWS, Terraform, Kubernetes), Linux systems, containerization (Docker), and workflow orchestration (Airflow).
Strong software engineering skills including Python, Git, unit testing, CI/CD, and understanding of MLOps lifecycle and software development best practices.
Experienced with operationalizing ML models at scale in cloud-based environments using infrastructure automation and container orchestration tools.
Comfortable working independently and collaboratively within multidisciplinary teams spanning data science and cloud engineering.
Demonstrates strong problem-solving ability with attention to detail and commitment to production-quality, reliable deployments.