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Popular AI title, mid-level seniority, and broad ML/MLOps skillset raise competition.
ML engineering and MLOps skills are moderately transferable across industries but need domain expertise.
Requires specific ML, MLOps, and production coding skills, causing strict technical filtering.
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Design, train, and fine-tune machine learning and deep learning models for production use.
Build and maintain efficient data pipelines for large-scale data ingestion and preprocessing.
Deploy models to cloud infrastructure with continuous performance monitoring and optimize model inference and resource use.
Proficient in Python and C++ programming languages.
Experience with machine learning and deep learning model development and deployment.
Work Experience Required: Not explicitly mentioned in the JD
Experience with cloud infrastructure and MLOps practices.
Experienced in production-grade code development for system integration of AI models.
Skilled in optimizing model performance and resource utilization.
Familiar with end-to-end ML pipeline engineering including data preprocessing to deployment.