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Remote hiring and a popular AI/ML title attract broad applicant pools despite modest brand.
Core ML engineering skills transfer across industries, so background fit sensitivity is low.
Moderate technical requirements and many preferred ML/MLOps tools imply medium screening strictness.
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Clean, annotate, and pre-process datasets for supervised learning models, develop and test simple ML models under guidance.
Assist and contribute to ML module development, deployment, data pipeline support, unit testing, and technical documentation.
Participate in model training, debugging, and support knowledge sharing sessions; responsible for development and maintenance of smaller AI modules.
Bachelor’s degree in Computer Science, Data Science, IT, or related field; Master’s preferred or equivalent experience for senior levels.
Work Experience Required: Not explicitly mentioned in the JD.
Familiarity with Python programming and basic AI/ML tools such as Jupyter, scikit-learn, or TensorFlow.
Knowledge of ML techniques (regression, classification, clustering), cloud ML services, and basic ML/DL principles.
Comfortable working in Agile or DevOps workflows with experience in rapid learning and applying new AI concepts and tools.
Able to translate business problems into AI/ML solutions and communicate technical results to non-technical stakeholders.
Practical experience in end-to-end AI/ML solution development including data engineering, model deployment, monitoring, and applying domain knowledge (e.g., IT operations).