MLOps Engineer
Tasks
- Automate machine learning CI CD workflows
- Build model serving infrastructure
- Create monitoring logging alerting observability
- Deploy and optimize production machine learning systems
- Design feature stores and data pipelines
- Develop machine learning pipelines
- Implement deployment strategies
- Maintain platform scalability security governance
- Manage scalable storage architectures
- Optimize GPU efficiency autoscaling and cloud resources
- Optimize inference for low latency and high throughput
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | AWS | Azure | B testing | Bash | CI/CD | Canary Releases | Databricks | Datadog | FastAPI | GCP | GitOps | Go | Grafana | Helm | High Throughput | Infrastructure as Code | KServe | Kubernetes | Low Latency | MLOps | Machine Learning | Model Serving | NVIDIA Triton | Observability | OpenTelemetry | Orchestration | Prometheus | PyTorch | Python | SRE | Snowflake | TensorFlow | TensorFlow Serving | Terraform | Torchserve | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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