ML Engineer

Role overview

We are seeking an ML Engineer in Doha, Qatar to turn machine-learning models into dependable, production-ready systems. This position focuses on classical machine-learning workflows and the operational side of deploying, maintaining, and improving models at scale rather than generative-AI application building.

What you should bring

• At least 4 years of experience in software engineering or machine-learning engineering, with direct, hands-on work deploying models into production.
• A proven record of taking models created by data scientists and converting them into stable, scalable, and observable production services.
• Practical production experience with traditional machine-learning workloads.
• Strong ability to work across data science, software engineering, and platform teams.
• A mindset focused on reliability, clean engineering practices, and long-term maintainability.
• Experience with feature stores and data pipelines that operate at scale or in streaming environments, where available.
• Familiarity with infrastructure-as-code tools such as Terraform and with reducing cloud spend through cost optimization.
• Exposure to workflow orchestration tools such as Apache Airflow.
• Awareness of model-serving frameworks and how to design APIs for inference workloads.
• Some exposure to generative-AI or LLM deployment is a plus.
• Prior experience in regulated or enterprise environments is advantageous.
• Cloud or MLOps certifications are considered a plus.

What you will do

• Develop, deploy, and support end-to-end production machine-learning pipelines, covering data ingestion, feature preparation, and model serving.
• Bring data scientist-built models into production while ensuring dependable performance, scalability, reproducibility, and reliability.
• Put MLOps practices in place, including CI/CD for ML, versioning for models, automated retraining, and use of model registries.
• Build and maintain feature pipelines and feature stores when needed.
• Create monitoring and alerting for model quality, data drift, and system reliability, then act quickly when performance declines.
• Improve inference speed, throughput, and infrastructure efficiency for deployed models.
• Package and run ML workloads using Docker and Kubernetes on Oracle Cloud Infrastructure (OCI).
• Reduce manual work by automating and strengthening data and model workflows.
• Partner with data scientists on model handover, packaging, and definition of success measures.
• Coordinate with software engineers to embed models into customer-facing products and services.
• Use software-engineering best practices such as testing, code reviews, and documentation in ML systems.
• Diagnose and fix issues across the model-serving stack in production.

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, or a related discipline; equivalent experience may also be considered.
• Strong Python development skills and production-level experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Hands-on experience with MLOps tools such as MLflow or Kubeflow, plus CI/CD pipelines.
• Solid knowledge of containerization and orchestration tools, including Docker and Kubernetes.
• Experience creating and maintaining data pipelines and handling large datasets.
• Background with cloud infrastructure, ideally Oracle Cloud Infrastructure (OCI) and OCI Data Science.
• Understanding of model monitoring, drift detection, and retraining approaches.
• Strong software-engineering fundamentals, including testing and version control.

Additional information

This is a full-time, onsite role based in Doha, Qatar. No salary, stipend, start date, application deadline, or number of vacancies was specified.