MLOps Platform Engineer for AI Innovation Lab

Salt Digital Recruitment; Abu Dhabi; On-site; AED 180,000 - 240,000; Full time

Job summary

Salt Digital Recruitment is seeking to join a brand-new project in an Innovation Hub/AI Lab to design and develop a ML platform that empowers the Data Science and AI team to scale experimentation and speed up path to production.

The role focuses on CI/CD/CT pipelines, model serving architectures for ultra-low latency real-time APIs and large-scale batch inference, and on monitoring, governance, and auditable lineage of ML artifacts using MLflow and Kubeflow.

Qualifications

• 3-7 years of experience building and operating high-availability ML pipelines and infrastructure.
• Strong programming skills in Python, Go or Java and Linux scripting.
• Hands-on experience with MLflow, Kubeflow or Argo workflows and model registry tooling.
• Experience with cloud security, IAM, VPC isolation and encryption at rest/in transit.

Responsibilities

• Design and automate robust ML pipelines for CI/CD/CT of models.
• Configure model serving architectures for real-time and batch inference.
• Implement monitoring, logging and data drift detection in production.
• Manage model registries and reproducible artifact lineage (MLflow/Kubeflow).
• Scale containerized apps with Docker and Kubernetes or managed services.
• Collaborate with security and data teams on data governance and encryption policies.

Skills

Python

Go

Java

Linux

CI/CD

MLOps

Security

Tools

MLflow

Kubeflow

Argo Workflows

Feast

SageMaker

Azure ML

Docker

Kubernetes

Terraform

CloudFormation

Job description

Salt Digital Recruitment is seeking to join a brand-new project in an Innovation Hub/AI Lab to design and develop a ML platform that empowers the Data Science and AI team to scale experimentation and speed up path to production.

The role focuses on CI/CD/CT pipelines, model serving architectures for ultra-low latency real-time APIs and large-scale batch inference, and on monitoring, governance, and auditable lineage of ML artifacts using MLflow and Kubeflow.