MTS - AI / MLOps
Tasks
- Architect AI ML reference architectures
- Automate end to end MLOps workflows
- Build infrastructure as code for AI ML environments
- Collaborate with data scientists and product management on MLOps strategy
- Create technical documentation and demos
- Design MLOps pipelines
- Implement model serving with NVIDIA Triton
- Optimize GPU inference for low latency high throughput
- Optimize models using quantization and sharding
- Set up automated GPU enabled MLOps lab environments
Perks/Benefits
Skills/Tech-stack
Ansible | CI/CD | CUDA | Container Orchestration | DeepSpeed | Dynamic batching | GPU resource management | Git | Inference Server | Infrastructure as Code | Jenkins | Kubeflow | Kubernetes | MLOps | MLflow | Machine Learning | Megatron-LM | Model sharding | NVIDIA Triton | NVIDIA Triton Inference | NVIDIA Triton Inference Server | NumPy | Pandas | PyTorch | Python | Quantization | Ray | Resource Management | Terraform | Triton Inference Server | “as-code”
Education
N/A
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