{"schemaVersion":"jobsearcher.job.v1","id":"283a48ba02ebf615e4733cc7","url":"https://jobsearcher.com/jobs/283a48ba02ebf615e4733cc7","canonicalUrl":"https://jobsearcher.com/jobs/283a48ba02ebf615e4733cc7","title":"MLOps Engineer","description":"Location: Remote (working in CST hours)\n\nProject Details: Own the end-to-end lifecycle of production ML: training, packaging, deployment, monitoring, and governance. Build reusable pipelines and tooling so data scientists and contractors can ship reliable model quickly - batch and real-time - on Google cloud.\n\nMust Have Skills:\n4+ years of MLOps/ML platform or DevOps for data/ML systems\nHands on GCP experience: BigQuery, Cloud Run, Cloud Storage, Pub/Sub, Cloud Build (Vertex AI a plus)\nProficiency with Python, packaging (Docker), and CI/CD\nSolid SQL skills and understanding of data modeling for ML features/labels\nExperience operating production models with monitoring, alerting, and incident response\n\nSoft Skills:\nNice to have Skills:\nModel registry & experiment tracking (ML Flow, W&B, or Vertex AI)\nData validation & monitoring (Great Expectations, TensorFlow Data Validation, WhyLabs, Arize)\nFeature store concepts (BQ-based or managed)\nCanary/shadow deployments, autoscaling, and performance tuning\nIaC (Terraform), testing frameworks (unit/integration/lead), and observability (Open Telemetry, Cloud Monitoring)\n\nEducation/certification requirements:\nN/A\n\nDay to Day responsibilities:\nPipelines & orchestration: Design CI/CD and scheduled pipelines for training and inference (Cloud Build, Workflows/Scheduler, Pub/Sub, Cloud Run; Vertex Pipelines if used).\nPackaging & deployment: Standardize model packaging (Docker), artifact/versioning, and rollout strategies (A/B, canary, shadow) with automated rollbacks.\nData/feature flows: Define contracts for features/labels in BigQuery and manage backfills; support batch and (where applicable) streaming features.\nRegistry & experimentation: Stand up a model registry and experiment tracking (MLflow/Weights & Biases/Vertex) with approvals and audit trails.\nMonitoring & quality: Implement data/feature validation, drift/decay monitoring, performance/latency SLOs, and alerting; build dashboards and playbooks.\nSecurity & compliance: Enforce IAM least privilege, service accounts, Secrets Manager, provenance/lineage, and change management.\nCost & performance: Track training/inference cost and latency; optimize hardware/ autoscaling and query patterns.\nEnablement: Create templates, docs, and tooling so DS/contractors can add models with minimal friction.\n\nTech stack you’ll use\nCompute/Orchestration: Cloud Run, Workflows/Scheduler, Pub/Sub, Vertex Pipelines (optional)\nData/Storage: BigQuery, Cloud Storage (artifacts, datasets)\nCI/CD & IaC: Cloud Build or GitHub Actions, Terraform\nML Tooling: MLflow/W&B/Vertex, Docker, PyTorch/TF/XGBoost (as provided by DS)\nMonitoring: Cloud Logging/Monitoring, Evidently/WhyLabs/Arize, custom run IDs & metrics\nHow we work\nSmall, versioned releases; test-first pipelines; documented runbooks.\nClear SLOs and blameless incident reviews.\nClose partnership with Data Engineering and Data Science; contracts over assumptions\n\nCompensation\nHourly Rate Range - $40-$60/ hr\n\nBenefits Offered:\n[Health, Dental, Vision Insurance]\n\nDeadline: Applications accepted until 10/30/2025 at 11:59 PM CST\n\nWe are an Equal Pay Employer. All employment decisions, including compensation, benefits, hiring, training, and promotions, are made based on merit, qualifications, and business needs. We do not discriminate on the basis of gender, race, ethnicity, age, disability, sexual orientation, or any other protected characteristic. We are committed to ensuring equal pay for equal work and regularly review our compensation practices to promote fairness, equity, and transparency across our organization.","company":"Cedent","rawCompany":"cedent","city":"Remote","state":"OR","isRemote":false,"isActive":false,"createdAt":"2026-08-11T14:52:43.877Z","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":"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":"MLOps Engineer","description":"Location: Remote (working in CST hours)\n\nProject Details: Own the end-to-end lifecycle of production ML: training, packaging, deployment, monitoring, and governance. Build reusable pipelines and tooling so data scientists and contractors can ship reliable model quickly - batch and real-time - on Google cloud.\n\nMust Have Skills:\n4+ years of MLOps/ML platform or DevOps for data/ML systems\nHands on GCP experience: BigQuery, Cloud Run, Cloud Storage, Pub/Sub, Cloud Build (Vertex AI a plus)\nProficiency with Python, packaging (Docker), and CI/CD\nSolid SQL skills and understanding of data modeling for ML features/labels\nExperience operating production models with monitoring, alerting, and incident response\n\nSoft Skills:\nNice to have Skills:\nModel registry & experiment tracking (ML Flow, W&B, or Vertex AI)\nData validation & monitoring (Great Expectations, TensorFlow Data Validation, WhyLabs, Arize)\nFeature store concepts (BQ-based or managed)\nCanary/shadow deployments, autoscaling, and performance tuning\nIaC (Terraform), testing frameworks (unit/integration/lead), and observability (Open Telemetry, Cloud Monitoring)\n\nEducation/certification requirements:\nN/A\n\nDay to Day responsibilities:\nPipelines & orchestration: Design CI/CD and scheduled pipelines for training and inference (Cloud Build, Workflows/Scheduler, Pub/Sub, Cloud Run; Vertex Pipelines if used).\nPackaging & deployment: Standardize model packaging (Docker), artifact/versioning, and rollout strategies (A/B, canary, shadow) with automated rollbacks.\nData/feature flows: Define contracts for features/labels in BigQuery and manage backfills; support batch and (where applicable) streaming features.\nRegistry & experimentation: Stand up a model registry and experiment tracking (MLflow/Weights & Biases/Vertex) with approvals and audit trails.\nMonitoring & quality: Implement data/feature validation, drift/decay monitoring, performance/latency SLOs, and alerting; build dashboards and playbooks.\nSecurity & compliance: Enforce IAM least privilege, service accounts, Secrets Manager, provenance/lineage, and change management.\nCost & performance: Track training/inference cost and latency; optimize hardware/ autoscaling and query patterns.\nEnablement: Create templates, docs, and tooling so DS/contractors can add models with minimal friction.\n\nTech stack you’ll use\nCompute/Orchestration: Cloud Run, Workflows/Scheduler, Pub/Sub, Vertex Pipelines (optional)\nData/Storage: BigQuery, Cloud Storage (artifacts, datasets)\nCI/CD & IaC: Cloud Build or GitHub Actions, Terraform\nML Tooling: MLflow/W&B/Vertex, Docker, PyTorch/TF/XGBoost (as provided by DS)\nMonitoring: Cloud Logging/Monitoring, Evidently/WhyLabs/Arize, custom run IDs & metrics\nHow we work\nSmall, versioned releases; test-first pipelines; documented runbooks.\nClear SLOs and blameless incident reviews.\nClose partnership with Data Engineering and Data Science; contracts over assumptions\n\nCompensation\nHourly Rate Range - $40-$60/ hr\n\nBenefits Offered:\n[Health, Dental, Vision Insurance]\n\nDeadline: Applications accepted until 10/30/2025 at 11:59 PM CST\n\nWe are an Equal Pay Employer. All employment decisions, including compensation, benefits, hiring, training, and promotions, are made based on merit, qualifications, and business needs. We do not discriminate on the basis of gender, race, ethnicity, age, disability, sexual orientation, or any other protected characteristic. We are committed to ensuring equal pay for equal work and regularly review our compensation practices to promote fairness, equity, and transparency across our organization.","datePosted":"2026-08-11T14:52:43.877Z","dateModified":"2026-08-11T14:52:43.877Z","hiringOrganization":{"@type":"Organization","name":"Cedent","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote","addressRegion":"OR","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"283a48ba02ebf615e4733cc7"},"url":"https://jobsearcher.com/jobs/283a48ba02ebf615e4733cc7"}}