AI Platform Engineer
Tasks
- Build data and model pipelines with orchestration
- Build model training fine tuning and inference pipelines
- Create templates SDKs CLIs and documentation
- Define SLOs, SLIs, and incident response patterns
- Design scalable AI and ML platform
- Develop CI CD pipelines for models prompts and data
- Enforce secrets management IAM and network security
- Implement cost tracking and cost per workload reporting
- Implement data validation and schema enforcement
- Implement logging tracing and metrics
- Implement model governance and risk guardrails
- Implement reliable production rollout with blue green canary and shadow
- Manage model registry feature store and lineage
- Optimize compute autoscaling GPU usage and batching
- Set up monitoring for data drift model drift and prompt performance
Perks/Benefits
- N/A
Skills/Tech-stack
Airflow | Amazon SageMaker | Argo CD | Argo Workflows | Autoscaling | Azure DevOps | Azure Machine Learning | BentoML | Blue-Green Deployment | Blue/green | CI/CD | CUDA | Caching | Canary Deployment | CloudFormation | Dagster | Data Drift | Data Validation | Docker | Feature Store | FinOps | GPU Optimization | GitHub Actions | GitLab CI | Grafana | Helm | IAM | Jenkins | Knowledge Distillation | Kubeflow | Kubernetes | Latency monitoring | Loki | MLflow | Machine Learning | Model Deployment | Model Drift | Network Security | ONNX | OpenTelemetry | Prefect | Prometheus | Pulumi | Python | Quantization | Ray | Schema Enforcement | Secrets management | Shadow Deployment | TensorRT | Terraform | Vector Database | Vector Search | Vertex AI | Weights and Biases
Education
N/A
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