Senior MLOps Engineer – Scottsdale, United States. We are seeking a Senior MLOps Engineer to lead the design and deployment of production-grade machine learning systems and drive the maturity of our ML engineering and MLOps capabilities. In this role you will own key ML workstreams end to end, make architectural decisions, mentor other engineers, and collaborate with cross-functional teams to ensure ML solutions are scalable, reliable, and aligned with business objectives. Responsibilities include: lead the design, development, and production deployment of complex ML systems across domains such as NLP, computer vision, recommendations, and forecasting; own the end-to-end ML lifecycle from problem framing and data strategy through model development, validation, deployment, and monitoring; architect scalable, fault-tolerant ML pipelines using modern orchestration and serving frameworks; drive the adoption and maturity of MLOps practices including CI/CD for ML, automated retraining, model registry, and governance; define and enforce engineering standards for model development, testing, code quality, and documentation; evaluate and introduce new tools to improve model performance, pipeline efficiency, and developer productivity; collaborate with data engineers, platform engineers, and product teams to align ML infrastructure with organizational goals; mentor and provide technical guidance to mid-level and junior engineers; conduct design reviews, code reviews, and architectural assessments for ML systems; contribute to technical roadmap planning and communicate tradeoffs and re

Senior MLOps Engineer – Scottsdale, United States. We are seeking a Senior MLOps Engineer to lead the design and deployment of production-grade machine learning systems and drive the maturity of our ML engineering and MLOps capabilities. In this role you will own key ML workstreams end to end, make architectural decisions, mentor other engineers, and collaborate with cross-functional teams to ensure ML solutions are scalable, reliable, and aligned with business objectives. Responsibilities include: lead the design, development, and production deployment of complex ML systems across domains such as NLP, computer vision, recommendations, and forecasting; own the end-to-end ML lifecycle from problem framing and data strategy through model development, validation, deployment, and monitoring; architect scalable, fault-tolerant ML pipelines using modern orchestration and serving frameworks; drive the adoption and maturity of MLOps practices including CI/CD for ML, automated retraining, model registry, and governance; define and enforce engineering standards for model development, testing, code quality, and documentation; evaluate and introduce new tools to improve model performance, pipeline efficiency, and developer productivity; collaborate with data engineers, platform engineers, and product teams to align ML infrastructure with organizational goals; mentor and provide technical guidance to mid-level and junior engineers; conduct design reviews, code reviews, and architectural assessments for ML systems; contribute to technical roadmap planning and communicate tradeoffs and re