Principal Machine Learning Engineer
Tasks
- Apply security principles across AI lifecycle
- Architect ML platform vision
- Build model training and inference services
- Define MLOps and AIOps best practices
- Design and implement ML workflow orchestration
- Drive automation and infrastructure-as-code
- Implement CI/CD pipelines
- Implement observability stack
- Lead and mentor ML systems team
- Partner with data science and engineering teams to plan platform roadmap
Perks/Benefits
Skills/Tech-stack
AWS | Alerting | Argo Workflows | Automated testing | Azure | CI/CD | Distributed Systems | Feature Store | Flyte | GCP | GPU | Git | Infrastructure as Code | Istio | Kubeflow Pipelines | Kubernetes | LLMOps | MLOps | Machine Learning | Model Registry | Model Serving | Monitoring | Pulumi | Python | RAG | Service Mesh | Terraform | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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