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...