Senior MLOps Architect: Scale ML Infra & Reliability

TALENTMATE; Abu Dhabi; On-site; AED 360,000 - 600,000; Full time

Job summary

AI71 in Abu Dhabi is seeking an Executive-Level MLOps Engineer to own ML infrastructure strategy, spanning SaaS and on-prem, including LLM deployment, fine-tuning and serving at scale.

You will mentor engineers, define deployment pipelines (MLflow, Kubeflow), monitor reliability, and drive cross-team initiatives to improve performance and cost efficiency.

Qualifications include 10+ years in MLOps, deep cloud expertise, Python, and experience with on-prem air-gapped environments.

Qualifications

• 10+ years in MLOps or ML infrastructure with architectural ownership.
• Proven track record deploying large-scale models (LLMs) at scale.
• Deep cloud expertise across AWS, Azure, or GCP and Python proficiency.
• Mentorship of engineers to operate independently at higher levels.
• Experience with SaaS and on-prem / air-gapped deployments.
• Kubernetes at architectural depth including GPU scheduling and multi-tenancy.
• Strong communication and stakeholder management for diverse teams.

Responsibilities

• Define ML infra architecture across deployment, pipelines, and cloud infra.
• Set reliability targets: monitoring, latency, availability, incident response.
• Mentor senior MLOps engineers across teams.
• Drive cross-team initiatives to improve inference performance and cost-efficiency.
• Partner with ML researchers and product leadership on multi-quarter infra strategy.
• Ensure infra scales across SaaS and air-gapped on-prem deployments.

Skills

MLOps

ML infrastructure

Python

Kubernetes

Distributed training

Cloud platforms

Stakeholder management

Mentorship

Tools

Kubeflow

MLflow

Kubeflow

Slurm

Triton

DeepSpeed

FSDP

Accelerate

Megatron-LM

Job description

AI71 in Abu Dhabi is seeking an Executive-Level MLOps Engineer to own ML infrastructure strategy, spanning SaaS and on-prem, including LLM deployment, fine-tuning and serving at scale.

You will mentor engineers, define deployment pipelines (MLflow, Kubeflow), monitor reliability, and drive cross-team initiatives to improve performance and cost efficiency.

Qualifications include 10+ years in MLOps, deep cloud expertise, Python, and experience with on-prem air-gapped environments.