MLOps Engineer
Tasks
- Automate CI/CD workflows
- Build scalable data pipelines
- Design model-serving infrastructure
- Develop machine learning pipelines
- Enable autoscaling
- Ensure platform scalability security governance and best practices
- Implement deployment strategies
- Improve GPU efficiency
- Maintain feature stores
- Manage cloud resources
- Optimize inference latency and throughput
- Orchestrate model development and deployment
- Set up monitoring logging alerting observability
- Support model training and inference
Perks/Benefits
Skills/Tech-stack
AWS | Autoscaling | Azure | Bash | CI/CD | Data Pipelines | Datadog | FastAPI | Feature Stores | GCP | GitOps | Go | Grafana | Helm | Infrastructure as Code | KServe | Kubeflow | Kubernetes | MLflow | Machine Learning | NVIDIA Triton | OpenTelemetry | Prometheus | PyTorch | Python | TensorFlow | TensorFlow Serving | Terraform | Torchserve | Weights and Biases | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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