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Popular ML role with broad, non-niche skill requirements raises applicant competition.
Core ML skills are transferable, but IT-ops and cybersecurity domain knowledge increases role specificity.
Multiple mandatory ML, cloud, and MLOps skills increase filtering despite no explicit years requirement.
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Prepare and preprocess datasets for supervised learning and build simple machine learning models under guidance.
Develop, test, and maintain ML modules, assist in deployment and support data pipelines with unit-tested code.
Contribute to AI/ML solutions including architecture, training, deployment, and monitoring, focusing on scalable models and integration with ITSM systems.
Bachelor’s degree in Computer Science, Data Science, IT, or related field (Master’s preferred for senior levels).
Experience Required: Not explicitly mentioned in the JD.
Proficiency in Python programming and basic familiarity with ML tools like TensorFlow, scikit-learn, or Jupyter.
Knowledge of supervised and unsupervised ML methods and ability to apply them in projects.
Engineer skilled in end-to-end AI/ML development including dataset preparation, model building, deployment, and monitoring.
Experience or strong familiarity with cloud-native AI services and containerization tools (Docker, Kubernetes) for MLOps roles.
Able to translate business problems into AI/ML solutions and communicate effectively with technical and non-technical stakeholders.