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Strong global brand, mid-level MLOps role in metro with generalist requirements increases applicant competition.
MLOps, cloud deployment, and containerization skills transfer easily across industries and roles.
Explicit 3-4 years requirement plus mandatory MLOps, cloud, Kubernetes and CI/CD skills makes screening strict.
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Deploy and manage machine learning models within the Global Mobility Solution platform at scale.
Build and maintain ML operations including model artifact versioning, serving, inference pipelines, monitoring, and performance benchmarking.
Collaborate cross-functionally to transform proof-of-concept AI models into production-ready solutions while optimizing reliability and performance.
3-4 years of experience deploying, monitoring, and maintaining machine learning models in cloud environments.
Bachelor's degree in Computer Science; Master's degree preferred.
Proficient with cloud platforms such as AWS, Terraform, and Kubernetes.
Experienced in Linux-based systems, containerization (Docker), workflow orchestration (Airflow), and software engineering practices (Python, Git, CI/CD).
Experienced in building scalable production ML infrastructure and operationalizing machine learning lifecycle (MLOps) at an enterprise level.
Technical proficiency spanning cloud-native services, distributed computing, containerized deployment, and automation tools.
Able to work independently and collaboratively with data scientists, cloud engineers, and data engineers to optimize AI model productionalization in complex environments.