MLOps Engineer

2 weeks ago

Vancouver, British Columbia, Canada Spait Infotech Private Limited Full time

Key ResponsibilitiesModel Deployment & Operations

  • Deploy machine learning models into production environments using CI/CD pipelines.
  • Build scalable serving architectures using tools such as TensorFlow Serving, TorchServe, FastAPI, or Kubernetes.
  • Automate model packaging, versioning, and rollout strategies (blue/green, canary).

Pipeline Automation

  • Develop end-to-end ML pipelines for data ingestion, preprocessing, training, testing, and deployment.
  • Use tools like Kubeflow, MLflow, Airflow, Prefect, or Vertex AI Pipelines.
  • Automate experiment tracking, hyperparameter tuning, and reproducibility.

Infrastructure & Cloud

  • Build and maintain ML infrastructure on cloud platforms (AWS, Azure, GCP).
  • Manage compute resources (EC2, GKE, AKS, SageMaker, Databricks).
  • Use Infrastructure-as-Code tools (Terraform, CloudFormation, Helm) for environment setup.

Model Monitoring & Maintenance

  • Implement monitoring for model drift, data quality, latency, and performance.
  • Create dashboards and alerting systems using Prometheus, Grafana, ELK, or Cloud Monitoring.
  • Manage retraining workflows, model lifecycle, and continuous learning pipelines.

Collaboration & Best Practices

  • Work closely with data scientists, ML engineers, and platform teams to streamline model delivery.
  • Enforce MLOps best practices: version control, testing, observability, reproducibility, security.
  • Participate in code reviews and architecture discussions.

Required Qualifications

  • Bachelor's degree in Computer Science, Data Engineering, or related field.
  • 2–5+ years hands-on experience in MLOps, DevOps, or ML engineering.
  • Strong programming skills in Python and familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
  • Experience with:
  • Docker & Kubernetes
  • CI/CD tools (GitHub Actions, GitLab, Jenkins, Azure DevOps)
  • Model tracking tools (MLflow, Weights & Biases)
  • Knowledge of cloud platforms: AWS, GCP, or Azure.
  • Strong grasp of data engineering concepts, ETL pipelines, and distributed systems.

Job Type: Full-time

Pay: $62,245.32-$143,212.91 per year

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