{"schemaVersion":"jobsearcher.job.v1","id":"97de2800439f98244fb77a34","url":"https://jobsearcher.com/jobs/97de2800439f98244fb77a34","canonicalUrl":"https://jobsearcher.com/jobs/97de2800439f98244fb77a34","title":"MLOps Engineer","description":"Location: SFO, California, Duration: Long-Term Contract, Note: Local candidates only\n\nJob Overview\nWe are seeking an experienced MLOps Engineer to design, build, and maintain scalable machine learning operations pipelines that support the full model lifecycle—from development and training to deployment, monitoring, and retraining. This role focuses on enabling production-grade ML systems using modern cloud platforms, CI/CD practices, and MLOps frameworks , ensuring reliability, scalability, governance, and performance of machine learning models in enterprise environments.\n\nKey Responsibilities\nML Pipeline Development & Operations\n\nDevelop and maintain robust machine learning pipelines using frameworks such as MLflow, Kubeflow, or Vertex AI .\n\nAutomate the end-to-end ML lifecycle, including model training, validation, testing, deployment, and monitoring in cloud environments.\n\nImplement reusable and scalable workflows for model versioning, tracking, and retraining.\n\nCI/CD & Model Lifecycle Management\n\nDesign and implement CI/CD pipelines for machine learning models , ensuring seamless integration from development to production.\n\nManage model versioning, model registry, and deployment pipelines with strong governance practices.\n\nEnsure reproducibility and traceability across ML lifecycle stages.\n\nCloud, Containers & Deployment\n\nDeploy and manage ML workloads on cloud platforms such as GCP, AWS, or Azure .\n\nWork with containerization technologies like Docker and Kubernetes to provision scalable model serving environments.\n\nEnable low-latency model scoring APIs for real-time inference use cases.\n\nMonitoring, Governance & Compliance\n\nImplement model monitoring and observability frameworks to track performance, drift, and anomalies in production.\n\nEnsure compliance with model risk management (MRM) standards , including documentation, explainability, and audit readiness.\n\nEstablish alerts and feedback loops for continuous model improvement and retraining.\n\nCollaboration & Engineering Enablement\n\nCollaborate with data engineering and platform teams to build and optimize data pipelines and ML infrastructure .\n\nSupport engineering teams in provisioning scalable environments for ML model development and deployment.\n\nPartner with stakeholders to translate business requirements into ML-driven solutions.\n\nAutoML & Accelerated ML Development\n\nLeverage AutoML tools such as Vertex AI AutoML and H2O Driverless AI to accelerate model development and deployment.\n\nEnable low-code/no-code ML workflows where appropriate, while ensuring production-grade quality and governance.\n\nRequired Qualifications\n\n10+ years of experience in Software Engineering , with at least 3+ years focused on AI/ML and MLOps .\n\nStrong programming experience in Python and Java , along with SQL and ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch .\n\nHands-on experience with cloud platforms (GCP, AWS, or Azure) .\n\nStrong knowledge of containerization technologies (Docker, Kubernetes) .\n\nExperience with data engineering and workflow orchestration tools such as Airflow and Spark .\n\nSolid understanding of DevOps principles, CI/CD practices, and software engineering best practices .\n\nStrong communication skills with the ability to explain complex ML concepts to both technical and non-technical stakeholders.\n\nPreferred Qualifications\n\nExperience with Vertex AI, MLflow, Kubeflow, or similar MLOps platforms .\n\nFamiliarity with model governance frameworks (MRM, model documentation, explainability tools) .\n\nExperience building real-time inference systems and scalable ML APIs .\n\nExposure to enterprise-scale ML systems in regulated industries.\n\n#J-18808-Ljbffr","company":"Codinix Technologies","rawCompany":"codinix technologies","city":"Millbrae","state":"CA","isRemote":false,"isActive":true,"createdAt":"2026-10-29T03:50:59.007Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"MLOps Engineer","description":"Location: SFO, California, Duration: Long-Term Contract, Note: Local candidates only\n\nJob Overview\nWe are seeking an experienced MLOps Engineer to design, build, and maintain scalable machine learning operations pipelines that support the full model lifecycle—from development and training to deployment, monitoring, and retraining. This role focuses on enabling production-grade ML systems using modern cloud platforms, CI/CD practices, and MLOps frameworks , ensuring reliability, scalability, governance, and performance of machine learning models in enterprise environments.\n\nKey Responsibilities\nML Pipeline Development & Operations\n\nDevelop and maintain robust machine learning pipelines using frameworks such as MLflow, Kubeflow, or Vertex AI .\n\nAutomate the end-to-end ML lifecycle, including model training, validation, testing, deployment, and monitoring in cloud environments.\n\nImplement reusable and scalable workflows for model versioning, tracking, and retraining.\n\nCI/CD & Model Lifecycle Management\n\nDesign and implement CI/CD pipelines for machine learning models , ensuring seamless integration from development to production.\n\nManage model versioning, model registry, and deployment pipelines with strong governance practices.\n\nEnsure reproducibility and traceability across ML lifecycle stages.\n\nCloud, Containers & Deployment\n\nDeploy and manage ML workloads on cloud platforms such as GCP, AWS, or Azure .\n\nWork with containerization technologies like Docker and Kubernetes to provision scalable model serving environments.\n\nEnable low-latency model scoring APIs for real-time inference use cases.\n\nMonitoring, Governance & Compliance\n\nImplement model monitoring and observability frameworks to track performance, drift, and anomalies in production.\n\nEnsure compliance with model risk management (MRM) standards , including documentation, explainability, and audit readiness.\n\nEstablish alerts and feedback loops for continuous model improvement and retraining.\n\nCollaboration & Engineering Enablement\n\nCollaborate with data engineering and platform teams to build and optimize data pipelines and ML infrastructure .\n\nSupport engineering teams in provisioning scalable environments for ML model development and deployment.\n\nPartner with stakeholders to translate business requirements into ML-driven solutions.\n\nAutoML & Accelerated ML Development\n\nLeverage AutoML tools such as Vertex AI AutoML and H2O Driverless AI to accelerate model development and deployment.\n\nEnable low-code/no-code ML workflows where appropriate, while ensuring production-grade quality and governance.\n\nRequired Qualifications\n\n10+ years of experience in Software Engineering , with at least 3+ years focused on AI/ML and MLOps .\n\nStrong programming experience in Python and Java , along with SQL and ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch .\n\nHands-on experience with cloud platforms (GCP, AWS, or Azure) .\n\nStrong knowledge of containerization technologies (Docker, Kubernetes) .\n\nExperience with data engineering and workflow orchestration tools such as Airflow and Spark .\n\nSolid understanding of DevOps principles, CI/CD practices, and software engineering best practices .\n\nStrong communication skills with the ability to explain complex ML concepts to both technical and non-technical stakeholders.\n\nPreferred Qualifications\n\nExperience with Vertex AI, MLflow, Kubeflow, or similar MLOps platforms .\n\nFamiliarity with model governance frameworks (MRM, model documentation, explainability tools) .\n\nExperience building real-time inference systems and scalable ML APIs .\n\nExposure to enterprise-scale ML systems in regulated industries.\n\n#J-18808-Ljbffr","datePosted":"2026-10-29T03:50:59.007Z","dateModified":"2026-10-29T03:50:59.007Z","hiringOrganization":{"@type":"Organization","name":"Codinix Technologies","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"97de2800439f98244fb77a34"},"url":"https://jobsearcher.com/jobs/97de2800439f98244fb77a34"}}