MLOps Engineer – ML Platform

Build the backbone of scalable AI — power enterprise ML and GenAI solutions with robust MLOps engineering.

We are looking for engineering professionals who don’t just manage infrastructure, but enable scalable, secure, and production-ready AI ecosystems that drive enterprise innovation.

Expertise: Strong passion for building reliable ML platforms, automating AI workflows, and enabling seamless collaboration between data science, engineering, and infrastructure teams.

Proactive individuals who take ownership, solve complex technical challenges, and focus on reliability, scalability, governance, and operational excellence.

As an MLOps Engineer – ML Platform, you will design, implement, and operate enterprise-grade ML infrastructure and MLOps pipelines that enable scalable AI and GenAI solutions. You will collaborate with data scientists, data engineers, and infrastructure teams to productionize machine learning workflows, automate deployments, and ensure secure, governed AI operations across the enterprise.

Design and maintain scalable ML platform architecture including compute, storage, networking, and security in collaboration with infrastructure teams.

Build and manage CI/CD pipelines for ML workflows including model training, testing, deployment, rollback, and automated retraining processes.

Implement and manage model registry, experiment tracking, monitoring, and governance solutions to support enterprise AI operations.

Automate end-to-end data and model workflows including scheduling, batch scoring, real-time inference, and production orchestration.

Ensure compliance with security, regulatory, and governance standards across the ML lifecycle while improving platform reliability and operational efficiency.

Troubleshoot ML platform and pipeline issues, optimize performance, and enhance observability and monitoring capabilities.

Partner closely with data scientists, data engineers, and platform teams to productionize and scale ML and GenAI solutions effectively.

Strong academic background (Bachelor’s or Master’s in Computer Science, Engineering, or related fields).

Hands-on experience in MLOps, DevOps, ML Engineering, or Data Engineering roles with strong exposure to enterprise AI platforms.

Experience working with on-premise or hybrid cloud architectures supporting scalable AI/ML workloads.

Strong expertise in CI/CD tools such as GitLab CI, Jenkins, or similar automation platforms.

Hands-on experience with containerization and orchestration technologies including Docker, Kubernetes, or equivalent platforms.

Good understanding of machine learning workflows and frameworks such as scikit-learn, PyTorch, TensorFlow, or similar technologies.

Experience with ML platforms and tooling such as MLflow, Kubeflow, DataRobot, or similar solutions is an added advantage.

Exposure to banking or other highly regulated industries with strong focus on governance and compliance is preferred.

Familiarity with monitoring and observability stacks such as Prometheus, Grafana, ELK, or similar platforms is a plus.

Strong collaboration, problem-solving, and communication skills with the ability to explain technical trade-offs clearly to stakeholders.

Structured, process-driven mindset with strong focus on scalability, governance, reliability, and operational excellence.

Flexible to travel across Saudi Arabia for stakeholder discussions, workshops, and collaboration engagements (50% travel requirement).