MLOps Engineer

Location: Remote (working in CST hours) Project 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. Must Have Skills: 4+ years of MLOps/ML platform or DevOps for data/ML systems Hands on GCP experience: BigQuery, Cloud Run, Cloud Storage, Pub/Sub, Cloud Build (Vertex AI a plus) Proficiency with Python, packaging (Docker), and CI/CD Solid SQL skills and understanding of data modeling for ML features/labels Experience operating production models with monitoring, alerting, and incident response Soft Skills: Nice to have Skills: Model registry & experiment tracking (ML Flow, W&B, or Vertex AI) Data validation & monitoring (Great Expectations, TensorFlow Data Validation, WhyLabs, Arize) Feature store concepts (BQ-based or managed) Canary/shadow deployments, autoscaling, and performance tuning IaC (Terraform), testing frameworks (unit/integration/lead), and observability (Open Telemetry, Cloud Monitoring) Education/certification requirements: N/A Day to Day responsibilities: Pipelines & orchestration: Design CI/CD and scheduled pipelines for training and inference (Cloud Build, Workflows/Scheduler, Pub/Sub, Cloud Run; Vertex Pipelines if used). Packaging & deployment: Standardize model packaging (Docker), artifact/versioning, and rollout strategies (A/B, canary, shadow) with automated rollbacks. Data/feature flows: Define contracts for features/labels in BigQuery and manage backfills; support batch and (where applicable) streaming features. Registry & experimentation: Stand up a model registry and experiment tracking (MLflow/Weights & Biases/Vertex) with approvals and audit trails. Monitoring & quality: Implement data/feature validation, drift/decay monitoring, performance/latency SLOs, and alerting; build dashboards and playbooks. Security & compliance: Enforce IAM least privilege, service accounts, Secrets Manager, provenance/lineage, and change management. Cost & performance: Track training/inference cost and latency; optimize hardware/ autoscaling and query patterns. Enablement: Create templates, docs, and tooling so DS/contractors can add models with minimal friction. Tech stack you’ll use Compute/Orchestration: Cloud Run, Workflows/Scheduler, Pub/Sub, Vertex Pipelines (optional) Data/Storage: BigQuery, Cloud Storage (artifacts, datasets) CI/CD & IaC: Cloud Build or GitHub Actions, Terraform ML Tooling: MLflow/W&B/Vertex, Docker, PyTorch/TF/XGBoost (as provided by DS) Monitoring: Cloud Logging/Monitoring, Evidently/WhyLabs/Arize, custom run IDs & metrics How we work Small, versioned releases; test-first pipelines; documented runbooks. Clear SLOs and blameless incident reviews. Close partnership with Data Engineering and Data Science; contracts over assumptions Compensation Hourly Rate Range - $40-$60/ hr Benefits Offered: [Health, Dental, Vision Insurance] Deadline: Applications accepted until 10/30/2025 at 11:59 PM CST We 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.