Requirements
3+ years of experience in a MLOps, DevOps, Site Reliability Engineering (SRE), or Infrastructure Engineering role.Strong hands-on experience with a major cloud provider (AWS or GCP).Proficiency with containerization and orchestration tools (Docker, Kubernetes).Solid experience with Infrastructure as Code (e.g., Terraform, Ansible, CloudFormation).Experience setting up and managing CI/CD pipelines (e.g., GitHub Actions, Jenkins, GitLab CI).Strong scripting skills in Python or Bash.A solid understanding of the machine learning development lifecycle.Good to have: Direct experience with MLOps tools (e.g., DVC, BentoML, KServe, Kubeflow, Seldon Core, MLflow).Experience in the financial services or FinTech industry.Familiarity with data processing frameworks like Apache Spark or Airflow.Relevant cloud certifications.
Description
mylo is a fintech platform dedicated to helping millions of people and businesses thrive by providing accessible and responsible financial solutions. Whether you’re purchasing a mobile phone, a new jacket, a flight ticket, a comfy couch, or even covering school tuition, mylo enables you to buy now and pay later at thousands of points of sale across Egypt. Born out of B.TECH—Egypt’s leading electronics and appliances retailer with over 27 years of experience in offering buy now, pay later solutions—mylo brings a legacy of trust and innovation to the fintech space. All mylo products are fully Sharia-compliant, ensuring ethical and inclusive financial practices.About this roleYou will be the vital link between our AI models and our production environment. You will design, build, and manage the scalable, reliable, and automated infrastructure that powers the entire machine learning lifecycle. Your work in MLOps and DevOps will empower our AI team to deploy and iterate on models faster and more efficiently.ResponsibilitiesBuild and Maintain MLOps Pipelines: Design and implement robust CI/CD pipelines for training, evaluating, and deploying machine learning models.Manage Cloud Infrastructure: Provision, configure, and manage our cloud infrastructure (AWS, GCP, or Azure) using Infrastructure as Code (IaC) principles.Automate Everything: Automate the deployment, monitoring, and scaling of machine learning services and data pipelines.Containerization & Orchestration: Utilize Docker and Kubernetes to create and manage reproducible environments for our ML applications.Collaborate and Empower: Work closely with AI engineers and data scientists to understand their needs and create tools that streamline their workflows.Ensure System Health: Implement comprehensive monitoring and logging to ensure the high availability, performance, and security of our ML platform.