Location: ISD – Egypt Office
Experience: 2–3 years
Employment Type: Full-time
About the Role
We are looking for an MLOps Engineer to bridge the gap between our AI development and production environments. You will be responsible for designing, building, and maintaining the infrastructure required to train, deploy, and monitor our Smart Video Analytics models at scale. If you are passionate about containerization, automation, and ensuring AI models perform flawlessly in the real world, this role is for you.
What You’ll Do
- Pipeline Automation: Design and implement automated CI/CD pipelines specifically tailored for machine learning workflows (training, testing, and deployment).
- Model Deployment: Package and deploy complex computer vision models into highly scalable production environments using Docker and Kubernetes (K8s).
- Infrastructure Management: Manage and optimize robust Linux-based infrastructure to support heavy video processing and real-time AI inference.
- Monitoring & Maintenance: Implement monitoring solutions to track model performance, data drift, and system health in real-time, ensuring high availability of video streaming pipelines.
- Collaboration: Work tightly with software developers and AI researchers to streamline the transition of models from research to production.
What We’re Looking For
- Experience: 2–3 years of experience in MLOps, DevOps, or Software Engineering with a strong focus on machine learning infrastructure.
- DevOps & OS: Deep expertise in Linux administration, Docker containerization, and Kubernetes orchestration.
- CI/CD & Version Control: Proficiency with Git and modern CI/CD tools (e.g., GitLab CI, Jenkins, GitHub Actions).
- ML Lifecycle Tools: Hands-on experience with MLOps platforms and tracking tools (e.g., MLflow, Kubeflow, Weights & Biases).
- Programming: Strong scripting and programming skills, particularly in Python and Bash.
- AI/ML Knowledge: Familiarity with deploying models built in frameworks like PyTorch, TensorFlow, or OpenCV.
- Bonus: Experience handling continuous video streams (RTSP, WebRTC) or working with cloud-native video processing pipelines.
