Staff MLOPs engineer
Role Overview
Own infrastructure, reliability, and deployment strategy for deep learning platforms across managed cloud SaaS and air-gapped on-premises environments.
The role is described as Senior / Staff level and full-time, based in Abu Dhabi, UAE, with full relocation provided.
Key Skills for This Role
Full Job Posting
Role overview
Own infrastructure, reliability, and deployment strategy for deep learning platforms across managed cloud SaaS and air-gapped on-premises environments.
The role is described as Senior / Staff level and full-time, based in Abu Dhabi, UAE, with full relocation provided.
Responsibilities
- Architect scalable model serving with vLLM, Triton, or TGI; pipeline orchestration with MLflow or Kubeflow; and multi-cloud delivery across AWS, Azure, or GCP.
- Optimize latency, throughput, and cost using distributed training frameworks such as DeepSpeed, FSDP, and Megatron-LM.
- Build resilient ML infrastructure for cloud and secure, disconnected deployments.
- Set platform SLOs, incident response practices, and GPU cluster scheduling strategies in Kubernetes.
- Mentor engineers, set engineering standards, and collaborate with research teams on infrastructure roadmaps.
Requirements
- Bring 10+ years in MLOps or ML infrastructure with end-to-end architectural ownership.
- Have a proven track record deploying, fine-tuning, and serving LLMs at scale.
- Bring deep Kubernetes expertise, including GPU scheduling, operators, and multi-tenancy, plus multi-cloud fluency.
- Have hands-on experience with on-premises, bare-metal, or air-gapped ML environments.
- GPU systems depth (CUDA, NCCL, or NVLink), quantization (FP8 or AWQ), or Slurm cluster management is strongly preferred.
Offer
- Complete relocation, visa, and transition support to Abu Dhabi is offered.
- The posting describes a highly competitive tax-free salary and comprehensive benefits.
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