MLOps Engineer: Scale Production ML Pipelines

Inception42; Abu Dhabi Emirate; On-site; AED 350,000 - 550,000; Full time

Job summary

Inception42 in Abu Dhabi is seeking an MLOps Engineer to build and operate end-to-end ML pipelines, from data prep to deployment and production monitoring.

You will design CI/CD workflows, automate testing and release controls, and manage model lifecycle governance across experiments, registries, and deployment governance. Collaboration with data science and platform teams is essential.

Qualifications

• Experience building production ML or data platforms with deployment ownership.
• Hands-on CI/CD automation using Python and shell scripting.
• Cloud and data platform experience with Azure or equivalent.

Responsibilities

• Design, build, and operate end-to-end ML pipelines for data prep, training, validation, packaging, and deployment.
• Create CI/CD workflows for ML systems with automated testing and reproducible builds.
• Manage model lifecycle with experiment tracking, registries, and deployment governance.
• Deploy model-serving and inference workloads across prod and non-prod using containerised platforms.
• Develop reusable platform components to accelerate moving models from research to production.
• Instrument ML systems for quality, drift, latency, and infrastructure health.
• Automate infrastructure provisioning with code and secure access controls.
• Collaborate with data science, engineering, product, and platform teams to resolve production issues.

Skills

Python scripting

Shell scripting

CI/CD pipelines

Debugging

Communication skills

Tools

Docker

Kubernetes

Azure

Azure Machine Learning

AKS

Terraform

Prometheus

Grafana

Job description

Inception42 in Abu Dhabi is seeking an MLOps Engineer to build and operate end-to-end ML pipelines, from data prep to deployment and production monitoring.

You will design CI/CD workflows, automate testing and release controls, and manage model lifecycle governance across experiments, registries, and deployment governance. Collaboration with data science and platform teams is essential.