MLOps & Infrastructure Engineer

Company Description Whizz Tech is a full-service, research-focused Software House that helps clients position themselves in the market through data-driven strategies. The company supports brands in reaching their full potential by delivering innovative marketing solutions tailored to their unique goals and audiences. Services include social media management, an in-house design studio with UI/UX capabilities, search engine optimization, and media buying. Whizz Tech emphasizes analytics, experimentation, and performance measurement to continuously optimize campaigns and deliver measurable business impact.

Role Description The MLOps & Infrastructure Engineer is a full-time, hybrid role based in Cairo, with flexibility for partial work from home. This role involves designing, implementing, and maintaining the infrastructure required to deploy, monitor, and scale machine learning models in production. The engineer will build and manage CI/CD pipelines, automate deployment workflows, and ensure the reliability and security of cloud and on-premise environments. Day-to-day responsibilities include collaborating with data scientists and developers to operationalize ML models, optimizing resource usage, handling incident response and troubleshooting, and implementing observability tools for performance and reliability monitoring. The role also includes maintaining documentation, improving DevOps and MLOps practices, and contributing to infrastructure roadmap planning.

Qualifications

• Candidates should possess strong Infrastructure and System Administration skills, including experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization/orchestration tools (e.g., Docker, Kubernetes).
• Candidates should possess Networking and Network Security skills, including understanding of VPNs, firewalls, load balancers, and secure access controls.
• Candidates should possess advanced Troubleshooting skills for production systems, monitoring tools, and performance optimization across distributed environments.
• Experience with MLOps practices, including ML model deployment, CI/CD pipelines, model versioning, and tools such as MLflow, Kubeflow, or similar frameworks.
• Proficiency in scripting and programming (e.g., Python, Bash) and familiarity with infrastructure-as-code tools (e.g., Terraform, Ansible) is highly beneficial.
• Solid understanding of Linux-based systems, observability stacks (e.g., Prometheus, Grafana, ELK), and reliability engineering principles.
• Ability to collaborate effectively with cross-functional teams, manage priorities, and document processes clearly.
• Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience.