Senior MLOps Architect: Scale ML Infra & On-Prem/Cloud

Greenhouse Software, Inc.; Abu Dhabi; On-site; AED 300,000 - 420,000; Full time

Job summary

AI71 is seeking an experienced MLOps Engineer to lead ML infrastructure strategy across SaaS and on-prem deployments. You will own deployment architectures, model serving at scale, and reliability targets while mentoring engineers across teams.

You will drive cross-team initiatives for performance, cost efficiency, and scalable inference, partnering with researchers and leadership to shape multi-quarter ML infrastructure strategy in a distributed cloud/on-prem environment.

Qualifications

• 10+ years in MLOps, ML infrastructure, or ML engineering with architectural ownership.
• Proven track record deploying large-scale models, including LLMs, and building ML infra at scale.
• Deep cloud expertise across AWS/Azure/GCP and strong Python proficiency.
• Mentorship history; ability to lead engineers to operate independently.
• Experience with SaaS and on-prem deployments, including air-gapped environments.
• Kubernetes at architectural depth; GPU scheduling and distributed workloads.

Responsibilities

• Define ML infrastructure architecture: model deployment, pipelines, cloud-native infra.
• Set reliability targets: monitoring, latency, throughput, incident response.
• Mentor senior MLOps engineers and raise the operational bar.
• Drive cross-team initiatives for inference performance and cost-efficiency.
• Partner with researchers, product, and leadership on multi-quarter strategy.
• Ensure scales across managed SaaS and on-prem deployments.

Tools

MLflow

Kubeflow

vLLM

Triton

TGI

DeepSpeed

FSDP

Accelerate

Slurm

CUDA

NCCL

Megatron-LM

NVLink

InfiniBand

Job description

AI71 is seeking an experienced MLOps Engineer to lead ML infrastructure strategy across SaaS and on-prem deployments. You will own deployment architectures, model serving at scale, and reliability targets while mentoring engineers across teams.

You will drive cross-team initiatives for performance, cost efficiency, and scalable inference, partnering with researchers and leadership to shape multi-quarter ML infrastructure strategy in a distributed cloud/on-prem environment.