AI Ops / ML Ops Engineer

Role overview

You will be joining a client engagement through Dautom, supporting a global organization known for its focus on quality, innovation, and operational excellence. The assignment is centered on taking AI and machine learning solutions from development into stable, secure, and scalable production use.

What this role involves

The position calls for an experienced AI Ops / ML Ops Engineer who can deploy, observe, maintain, and fine-tune AI/ML systems in live environments. Strong hands-on knowledge of MLOps, DevOps, cloud services, Databricks, and end-to-end model lifecycle management is essential.

Core responsibilities

• Release machine learning models into production and keep them running reliably.
• Set up MLOps practices, CI/CD workflows, and automation to streamline delivery.
• Track model quality, data drift, platform health, and key operational signals.
• Investigate issues and provide support for AI/ML applications and related services.
• Manage model governance, version tracking, and readiness for audits.
• Strengthen the scalability, stability, and efficiency of AI platforms.
• Work with Data Science, IT, Architecture, Security, and business stakeholders.

Technical focus areas

Machine learning: fundamentals, supervised and unsupervised learning, deep learning, feature engineering, training and evaluation, time-series forecasting, and explainability using SHAP and LIME.

MLOps and DevOps: MLflow, model registry and versioning, machine-learning CI/CD, automated retraining, drift detection, Kubernetes, Docker, Jenkins or GitHub Actions or Azure DevOps, and infrastructure as code with Terraform.

Databricks: Lakehouse, Delta Lake, Unity Catalog, Feature Store, model serving, workflows, Delta Live Tables, structured streaming, and MLflow integration.

Generative AI / LLMOps: prompt engineering, retrieval-augmented generation, agentic AI, fine-tuning, LangChain or LangGraph or LlamaIndex, vector databases such as Pinecone, Weaviate, Chroma, or Databricks Vector Search, and foundation model / LLM evaluation.

AI operations: performance monitoring, data and concept drift detection, prompt monitoring, cost tracking, governance, security and compliance, observability, and root cause analysis with tools such as Prometheus, Grafana, Datadog, and Azure Monitor.

Cloud environment: Azure Machine Learning, Azure OpenAI Service, Azure AI Search, Azure Data Factory, Azure Databricks, and Azure Kubernetes Service (AKS).

Qualifications

A bachelor’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related discipline is required. The role calls for at least 7 years of experience across MLOps, DevOps, AI/ML deployment, cloud platforms, and production support. Professional certifications in Azure, Databricks, Kubernetes, DevOps, or MLOps are preferred. Arabic and English language ability is considered an advantage.

Preferred background

Experience with production AI/ML platforms, the Databricks ecosystem, Azure cloud environments, generative AI and LLMOps implementations, and enterprise-scale AI governance and monitoring will be especially valuable.

About Dautom

Dautom is an internationally known IT consulting company focused on innovation, quality, and a people-first approach. The organization helps businesses strengthen their technology teams with high-caliber talent and supports a workplace culture built on trust, respect, continuous learning, and career development.