Senior MLOps Engineer II (AI Native)
Tasks
- Automate CI CD and continuous training
- Build streaming data pipelines with Kafka and Flink
- Create lineage tracking for data code and model artifacts
- Deploy and scale machine learning models
- Design build and scale MLOps pipelines
- Develop SDK and API integrations for ML systems
- Implement logging alerting and monitoring
- Mentor teams on ML engineering best practices
- Provision and optimize cloud ML infrastructure
Perks/Benefits
- 401k match
- Employee assistance program
- Health insurance
- Mental wellness program
- Paid parental leave
- RRSP/DPSP matching
- Remote-first
Skills/Tech-stack
AWS | Apache Airflow | Apache Ray | Apache Spark | Azure Storage | Concept drift | DBT | Dask | Data Drift | Databricks | Docker | FastAPI | Feast | Feature Store | Flink | GCP | Git | Google Kubernetes | Google Kubernetes Engine | Inference Server | Infrastructure as Code | Kafka | Kubeflow | Kubernetes | Kubernetes EKS | Kubernetes Engine | MLflow | Model Monitoring | PySpark | Python | SQL | TGI | Tecton | Terraform | Triton Inference | Triton Inference Server | VLLM | “as-code”
Education
Roles
Engineer | MLOps Engineer | Senior MLOps | Senior MLOps Engineer
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