Senior MLOps Engineer (Training & Inference Optimization)
Tasks
- Architect distributed training pipelines
- Conduct performance profiling and bottleneck analysis
- Deploy and orchestrate LLM inference
- Implement quantization optimization
- Lead engineering roadmap and code reviews
- Manage ML workloads with cluster schedulers
- Manage checkpointing and fault tolerance
- Mentor engineers
- Optimize GPU cost and scaling policies
- Optimize GPU utilization
- Standardize model tracking and versioning
- Tune throughput optimization techniques
Perks/Benefits
- N/A
Skills/Tech-stack
AWQ | C++ | CUDA | CUDA kernels | Continuous batching | FP8 | Flyte | Grafana | Infiniband | Jaeger | Kuberay | Kubernetes | MLOps | MLflow | Machine Learning | Megatron-Bridge | NCCL | NVIDIA Nemo | PagedAttention | Prometheus | PyTorch | Python | Quantization | Ray | RoCE | Rust | SGLang | SkyPilot | Slurm | TensorRT | TensorRT-LLM | Triton | VLLM
Education
N/A
Roles
Engineer | MLOps Engineer | Senior MLOps | Senior MLOps Engineer
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