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ML Ops Engineer
Career Insights for Machine Learning Engineer
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$168,439 / year median in California
Job Description
ML Ops Engineer Concord, CA 12 months contract Job Summary We are seeking an experienced ML Ops Engineer to design, build, and support scalable, secure, and production-ready machine learning platforms across cloud and on-premises environments. The ideal candidate will have strong expertise in MLOps, Kubernetes, cloud platforms, automation, and reliability engineering. Required Qualifications 8+ years of experience in Platform Engineering, DevOps, MLOps, or related fields. Strong experience with Google Cloud Platform (Google Cloud Platform) and cloud-native technologies. Hands-on expertise in Kubernetes, including GKE and/or OpenShift. Strong proficiency in Python for automation and platform development. Experience building and managing MLOps platforms and ML lifecycle workflows. Expertise in CI/CD pipelines and infrastructure automation. Knowledge of security, data protection, and compliance best practices. Experience with observability, monitoring, logging, and incident management. Strong understanding of Site Reliability Engineering (SRE) principles. Excellent communication and stakeholder management skills. Preferred Skills Experience designing enterprise-scale ML platform architectures. Multi-cloud experience (AWS, Azure, and Google Cloud Platform). Experience supporting AI/GenAI workloads in production environments. Knowledge of Infrastructure as Code (Terraform, Ansible, etc.). Familiarity with model serving, feature stores, and model monitoring. Experience mentoring engineers and driving platform engineering best practices. Background in highly regulated enterprise environments.