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Mid-level popular ML/MLOps role with broad cloud, Python, and observability requirements increases candidate competition.
Role requires ML-specific observability and model governance skills, reducing cross-industry transferability.
Explicit 5+ years and mandatory ML/MLOps, cloud, and Python skills create moderate hiring filters.
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Develop and maintain scalable, cloud-native AI observability solutions to ensure reliability and maintainability of AI systems.
Standardize observability practices (logging, metrics, tracing, model performance monitoring) across AI/ML and development teams.
Implement and extend DevOps/MLOps automation including infrastructure-as-code, CI/CD pipelines, data quality monitoring, and governance frameworks for AI systems.
Bachelor's degree in Computer Science, Data Science, Information Systems, or related field.
5+ years experience in DevOps, MLOps, Data Engineering, Software Engineering, or Site Reliability Engineering.
Strong cloud infrastructure knowledge with experience in at least one major cloud provider, preferably Azure.
Proficiency in an object-oriented programming language, preferably Python, with hands-on experience in ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Experienced in building automation-first DevOps and MLOps practices in complex AI/ML environments.
Capable of designing reusable, scalable patterns and frameworks for AI lifecycle management and observability.
Skilled in governance of AI systems including bias detection, explainability, auditability, and monitoring advanced ML model KPIs.