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Specialized MLOps observability skills and seniority reduce applicant density relative to generalist roles.
MLOps and observability skills transfer across industries but require specialized ML and cloud experience.
Explicit 8-10 years plus mandatory DevOps/MLOps/SRE and cloud skills enforces strict screening.
Implement scalable, cloud-native AI observability solutions for reliable and maintainable AI/ML systems in construction technology.
Standardize observability practices (logging, metrics, tracing, model monitoring) and lead automation-first DevOps/MLOps with infrastructure-as-code and CI/CD pipelines for ML workflows.
Design and manage DataOps pipelines with automated data quality and anomaly detection; monitor model KPIs and ensure governance including bias detection and explainability.
8-10 years experience in DevOps, MLOps, Data Engineering, Software Engineering, or Site Reliability Engineering.
Bachelor’s degree in Computer Science, Data Science, Information Systems, or related field.
Strong experience with cloud infrastructure (preferably Azure) and containerized ML workload deployment.
Proficiency in at least one object-oriented programming language (preferably Python) with experience in ML frameworks (TensorFlow, PyTorch, Scikit-learn).
Experienced in leading AI/ML observability and MLOps practices in production environments with large-scale, complex systems.
Skilled at designing reusable, scalable patterns and automation-first infrastructure-as-code CI/CD pipelines for ML workflows.
Familiar with governance and compliance aspects of AI including bias detection, explainability, auditability, and performance validation.