{"schemaVersion":"jobsearcher.job.v1","id":"0736eb33376e2eaaa3809102","url":"https://jobsearcher.com/jobs/0736eb33376e2eaaa3809102","canonicalUrl":"https://jobsearcher.com/jobs/0736eb33376e2eaaa3809102","title":"ML Ops Engineer","description":"ML Ops Engineer to drive the full lifecycle of machine learning solutions—from data exploration and model development to scalable deployment and monitoring. This role bridges the gap between data science model development and production-grade ML Ops Engineering.\r\nKey Responsibilities\r\nDevelop predictive models using structured/unstructured data across 10+ business lines, driving fraud reduction, operational efficiency, and customer insights.\r\nLeverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment\r\nDevelop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.\r\nAutomate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure).\r\nImplement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.\r\nMonitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)\r\nCollaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs\r\nQualifications\r\nStrong proficiency in Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).\r\nExperience with cloud platforms and containerization (Docker, Kubernetes).\r\nFamiliarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.\r\nSolid understanding of software engineering principles and DevOps practices.\r\nAbility to communicate complex technical concepts to non-technical stakeholders.\r\nJ-18808-Ljbffr","company":"TechDigital Group","rawCompany":"techdigital group","city":"San Leandro","state":"CA","isRemote":false,"isActive":true,"createdAt":"2026-06-25T01:12:01.614Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"ML Ops Engineer","description":"ML Ops Engineer to drive the full lifecycle of machine learning solutions—from data exploration and model development to scalable deployment and monitoring. This role bridges the gap between data science model development and production-grade ML Ops Engineering.\r\nKey Responsibilities\r\nDevelop predictive models using structured/unstructured data across 10+ business lines, driving fraud reduction, operational efficiency, and customer insights.\r\nLeverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment\r\nDevelop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.\r\nAutomate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure).\r\nImplement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.\r\nMonitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)\r\nCollaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs\r\nQualifications\r\nStrong proficiency in Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).\r\nExperience with cloud platforms and containerization (Docker, Kubernetes).\r\nFamiliarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.\r\nSolid understanding of software engineering principles and DevOps practices.\r\nAbility to communicate complex technical concepts to non-technical stakeholders.\r\nJ-18808-Ljbffr","datePosted":"2026-06-25T01:12:01.614Z","dateModified":"2026-06-25T01:12:01.614Z","hiringOrganization":{"@type":"Organization","name":"TechDigital Group","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Leandro","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"0736eb33376e2eaaa3809102"},"url":"https://jobsearcher.com/jobs/0736eb33376e2eaaa3809102"}}