AI Platform Engineer
Tasks
- Build model serving platforms
- Collaborate with ML and DevOps teams to deploy models
- Design AI inference platforms
- Develop monitoring observability logging distributed tracing alerting
- Implement autoscaling deployment monitoring
- Implement caching and high availability
- Implement cloud infrastructure optimization FinOps
- Implement security, authentication, authorization
- Manage model deployment versioning rollback lifecycle
- Optimize inference latency throughput GPU utilization
- Optimize request routing traffic management workload orchestration
Perks/Benefits
Skills/Tech-stack
API Gateway | AWS | Authentication | Authorization | Autoscaling | Bicep | C++ | CI/CD | CUDA | Cache optimization | Caching | Cloud platform | CloudFormation | Distributed Systems | 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 | Logging | MLOps | Microsoft Azure | Mixed Precision | Mixed precision inference | Model Compression | NVIDIA CUDA | Observability | Precision Inference | Pruning | Python | Quantization | Ray Serve | Rust | Security | 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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