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Mid-level, popular ML role in a metro with a well-known brand and broad skill requirements.
Core MLOps and cloud skills are broadly transferable across industries.
Explicit 3+ years and mandatory MLOps, cloud, and tooling requirements create strict filters.
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Own deployment of core data science and AI models into Global Mobility Solution platform.
Build and manage ML Operations platform including artifact versioning, serving, inference pipelines, and production integration.
Support monitoring, logging, and observability to ensure system reliability and model performance at scale.
Bachelor's degree in Computer Science; Master’s preferred.
3+ years of experience deploying, monitoring, and maintaining machine learning models in cloud environments.
Experience with cloud platforms (e.g., AWS), infrastructure as code (Terraform), container orchestration (Kubernetes), and MLOps lifecycle.
Strong software engineering skills (Python, Git, unit testing, CI/CD), Linux systems, containerization (Docker), workflow orchestration (Airflow), and data storage/processing (SQL, NoSQL, data lakes).
Experienced in collaborating with cross-functional teams including data scientists, cloud engineers, and data engineers to productionalize AI solutions.
Capable of transforming proof-of-concept machine learning models into scalable, production-ready solutions with performance benchmarking.
Comfortable working independently and managing end-to-end ML deployment in complex distributed cloud environments.