المسمى الوظيفي

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.

ML Engineer

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