AI Platform Engineer
Tasks
- Build scalable model serving systems
- Collaborate with ML and DevOps teams
- Deploy and manage model versions
- Design AI inference platform
- Drive cloud cost optimization FinOps
- Evaluate AI serving frameworks and GPU acceleration
- Implement autoscaling and workload orchestration
- Implement caching and high availability
- Implement monitoring observability logging and alerting
- Implement request routing and traffic management
- Implement security, authentication, and authorization
- Mentor engineering teams and conduct architecture reviews
- Optimize GPU utilization and memory management
- Optimize distributed AI workloads
- Optimize inference latency and throughput
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateway | Alerting | Authentication | Authorization | Autoscaling | C++ | CI/CD | CUDA | Caching | CloudFormation | Distributed Systems | Distributed tracing | Docker | FinOps | GPU Optimization | GitOps | Go | High Availability | Inference Server | Infrastructure as Code | Kubernetes | LLM serving | Logging | MLOps | Monitoring | NVIDIA GPU | Network routing | Observability | Python | Ray Serve | Rust | Security | 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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