DataOps Engineer (AI Platform Engineer)
Tasks
- Benchmark inference performance across runtimes
- Build model serving runtimes
- Build observability for GPU clusters
- Build reliable GPU infrastructure for model inference
- Conduct performance testing for GPU clusters
- Configure GPU MIG for workloads
- Deploy and scale machine learning models
- Design and operate on prem AI platform
- Enable model input and configuration tooling
- Ensure platform security compliance
- Implement CI/CD pipelines for model deployment
- Integrate tracing for inference workflows
- Maintain model serving performance and scalability
- Manage model lifecycle with versioning and registry
- Operate GPU operations in Kubernetes
- Support model fine tuning workflows
Perks/Benefits
- Annual leave
- Career development
- Education allowance
- Employee share scheme
- English lessons
- Family coverage
- Health insurance
- Life insurance
- Paid Maternity Leave
- Paid paternity leave
- Paid sick leave
- Relocation support
- Relocation visas assistance
- Unlimited learning
Skills/Tech-stack
AMD | CI/CD | Distributed Systems | Fine Tuning | GPU | Go | High Performance | High-performance networking | KServe | Kubernetes | LORA fine tuning | Linux | LoRA | Logging | MLflow | Machine Learning | Metrics | Model Serving | Monitoring | NVIDIA | NVIDIA Triton | Nvidia Triton Runtimes | ONNX | ONNX Runtime | Observability | Python | RDMA | SGLang | Tracing | VLLM
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
AI | AI Platform Engineer | DataOps Engineer | Engineer | Platform Engineer
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