AI Platform Engineer
Tasks
- Build model serving systems
- Conduct architecture reviews
- Deploy and monitor AI services
- Design AI inference platforms
- Ensure high availability
- Implement autoscaling
- Implement caching
- Implement observability
- Implement security controls
- Manage model deployment versioning rollback
- Mentor engineering teams
- Optimize GPU utilization
- Optimize cloud infrastructure cost using FinOps
- Optimize inference performance
- Orchestrate workloads and request routing
- Set up monitoring logging tracing alerting
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateway | AWS | Authentication | Authorization | Autoscaling | C++ | CI/CD | CUDA | Cache optimization | Caching | Cloud platform | Compression | Distributed Systems | Distributed tracing | Docker | Event Driven | Event-driven architecture | FinOps | GitOps | Go | Google Cloud | Google Cloud Platform | High Availability | Inference Server | Infrastructure as Code | Istio | KV cache | KV cache optimization | Kubernetes | Linkerd | Logging | MLOps | Microsoft Azure | Mixed Precision | NVIDIA GPU | Observability | Pruning | Python | Quantization | Ray Serve | Rust | Security | Service Mesh | Speculative decoding | TensorRT-LLM | Terraform | Triton Inference | Triton Inference Server | VLLM | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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