Must have

• Python

• English (B2)

Requirements description

Required Qualifications:

• Proven experience in MLOps, DevOps for AI, or ML platform engineering.
• Proficiency with Kubernetes, Docker, and workflow orchestration platforms.
• Strong engineering skills in Python.
• Experience with infrastructure-as-code tools (e.g., Terraform, Helm).
• Production deployment experience using SageMaker, Azure ML, or Vertex AI.
• Familiarity with data pipelines, feature stores, and cloud-native architectures.
• Expertise in CI/CD for ML, including version control, testing, and secure deployments.
• Strong cross-functional collaboration and problem-solving abilities.

Preferred Qualifications:

• Exposure to monitoring tools like Prometheus, Grafana, or similar.
• Familiarity with ML observability platforms.
• Background in compliance, governance, or model risk management in regulated industries.

Offer description

Role Overview

Our partner is a prominent Saudi Arabian conglomerate, recognized as one of the Middle East’s most influential family businesses. With operations in over 30 countries and a legacy spanning eight decades, the company has diversified across automotive, energy, finance, and other sectors. Known for global partnerships and philanthropy, they are now building cutting-edge AI/ML capabilities.

We are seeking a Senior ML Platform Engineer to design and scale robust, enterprise-grade AI/ML infrastructure. You will work at the intersection of machine learning, DevOps, and data engineering—bridging the gap between experimentation and production for AI models. Your contributions will support reliable, secure, and scalable ML deployments across diverse cloud environments.

Your responsibilities

1. Design, build, and scale MLOps pipelines to support training, evaluation, versioning, and deployment of ML models.
2. Manage containerized environments using Kubernetes and Docker.
3. Orchestrate workflows with Airflow, MLflow, Kubeflow, or similar tools.
4. Deploy and monitor ML models using cloud-native AI platforms (e.g., AWS SageMaker, Azure ML, Google Vertex AI).
5. Automate CI/CD pipelines with a focus on security, reproducibility, and reliability.
6. Collaborate with data scientists, ML engineers, and infrastructure teams for seamless model handovers.

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