AI Platform Engineer
Tasks
- Architect cloud native infrastructure for large language models
- Build autoscaling deployment and monitoring solutions
- Deploy and support production AI models
- Design scalable AI inference platforms
- Implement caching security and high availability strategies
- Implement infrastructure optimization and FinOps initiatives
- Implement observability logging distributed tracing and alerting
- Manage model deployment versioning rollback and lifecycle
- Mentor engineering teams and conduct architecture reviews
- Optimize inference performance GPU utilization request routing
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateways | AWS | Alerting | Authentication | Authorization | Autoscaling | C++ | CI/CD | CUDA | Cache optimization | Caching | Cloud platform | Compression | Distributed tracing | Docker | FinOps | GPU Optimization | GitOps | Go | Google Cloud | Google Cloud Platform | High Availability | Inference Server | Infrastructure as Code | KV cache | KV cache optimization | Kubernetes | LLM serving | Logging | MLOps | Microsoft Azure | Mixed Precision | NVIDIA GPU | Observability | Pruning | Python | Quantization | Ray Serve | Rust | 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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