MLOps Engineer

About the Role

****** is seeking a mid-to-senior MLOps Engineer to contribute to the development of the intelligence layer for its AI platform. This full-time position is based in Dhahran, Eastern Region. The role focuses on building underlying infrastructure to enable sophisticated AI workflows through a self-service product experience, rather than solely maintaining pipelines and deployments.

Role Context

The MLOps Engineer will operate at the intersection of platform engineering, machine learning infrastructure, distributed systems, and developer experience. This involves making model deployment, training, notebooks, functions, UI-driven agent creation, and RAG pipelines accessible and efficient for users.

Key Responsibilities

• Design and build the infrastructure that powers the intelligence layer.
• Enable reliable, automated workflows for model training, deployment, lifecycle management, and inference.
• Build scalable foundations for users to create, configure, and operate AI agents and RAG pipelines via the platform UI.
• Develop platform capabilities for managed notebooks, functions, experiments, training jobs, model registries, and serving endpoints.
• Improve model serving, observability, versioning, evaluation, promotion, and rollback capabilities.
• Optimize GPU inference and training deployments for performance, reliability, and cost efficiency.
• Explore efficient approaches for deploying models across centralized GPU infrastructure and edge devices.
• Automate workflows to reduce manual MLOps effort, emphasizing safe, self-service capabilities for platform users.
• Partner with backend, product, and AI teams to translate complex infrastructure into intuitive platform features.
• Help define standards for security, multi-tenancy, resource isolation, model governance, and operational reliability.

Technical Environment

The intelligence backend is developed in Rust. Experience with Rust is considered a strong advantage for this role.

Impact of the Role

Success in this position will involve transitioning the AI infrastructure from manual operations to a platform where users can train, evaluate, deploy, and operate models; work in managed notebooks; run functions; create agents; and configure RAG workflows. The platform will be designed to handle operational complexity, providing a streamlined user experience.