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Specialized MLOps skillset plus metro location and known brand yields moderate applicant competition.
Core ML and MLOps skills transfer across industries, but specific build/tooling and automotive context increase domain specificity.
Explicit 6-9 years plus mandatory ML, Python, CI/CD, AWS, CMake/Bazel requirements make filtering stringent.
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Design, develop, train, and deploy AI/ML models with end-to-end ML pipeline ownership including data preprocessing, model training, validation, deployment, and monitoring.
Develop Python-based automation for test suites, model validation, and workflow orchestration, integrating ML pipelines into CI/CD pipelines with tools like CMake and Bazel.
Deploy, monitor, and optimize ML workloads on AWS, collaborate with DevOps teams to implement MLOps best practices including model versioning and reproducibility.
6 to 9 years of hands-on AI/Machine Learning development experience.
Strong proficiency in Python for automation, ML model development, and testing.
Experience with AWS cloud services for ML deployment and DevOps practices including build, release, and deployment automation.
Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or related fields.
Experienced in integrating ML workflows into CI/CD pipelines and maintaining stable build environments using CMake and Bazel.
Familiar with DevOps tools and MLOps practices, ideally with exposure to containerization and orchestration (Docker, Kubernetes).
Comfortable working in multi-disciplinary teams delivering scalable, reliable AI solutions and capable of troubleshooting build, deployment, and performance issues across environments.