MLOps Engineer - Only W2

MLOps Engineer 12 Months Contract REMOTE, North Carolina Customer: CenterPoint Energy Job Description 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 models 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 (OpenTelemetry, Cloud Monitoring) 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