1062 | MLOps Engineer
Tasks
- Automate ML deployment pipelines
- Build optimize ML training retraining inference pipelines
- Collaborate with data scientists on MLOps architecture
- Design MLOps solutions
- Design platform level components
- Drive infrastructure automation CI CD
- Enhance observability with metrics logging tracing dashboards
- Implement scalable secure cost efficient MLOps pipelines
- Integrate security controls across ML lifecycle
- Manage cloud resource allocation
- Manage incidents end-to-end
- Perform root cause analysis
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | AWS SageMaker | Argo Workflows | ArgoCD | Autoscaling | CI/CD | Container Security | Databricks | Databricks Workflows | Docker | ELK | Feature Store | FluxCD | GitOps | GitOps workflows | Google Vertex | Google Vertex AI | Grafana | Kubeflow | Kubernetes | LLMOps | Langfuse | Large-scale | Large-scale inference | Loki | MLflow | Machine Learning | Model Deployment | Model Training | Ollama | Prometheus | Terraform | Terragrunt | Unity Catalog | VLLM | Vertex AI
Education
N/A
Roles
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