Senior MLops Engineer

About the role

About your role as MLops Engineer

• Yassir is seeking a highly skilled and motivated MLOps Engineer (of any senior level) to join our Artificial Intelligence (AI) team and drive our ML Operations in application deployment and infrastructure development enabling the training, deployment, experimentation, monitoring and altogether management of our ML applications and their artefacts, at scale and at speed. As an MLOps Engineer, you will play a crucial role in making Yassir’s products and operations more AI driven, through the use of modern technologies. This is a very exciting opportunity, tackling some of Yassir’s most critical problems.

About Us

• The AI team is part of Yassir’s central Data & AI domain, which is responsible for all data related activities in the organization - from Data Engineering, Data Platform management and Data Analytics all the way to AI solutions and MLOps infrastructure development. We are a global team of Data and AI / ML experts, encompassing diverse backgrounds and skills. We strive to build state-of-the-art Data and ML infrastructure and solutions, and ultimately a Data Culture supported by responsible and democratized data access, data-driven decision making, predictiveness and automation. We push the envelope of what’s feasible, we challenge each other, we support each other, and we take great pride in our work. Most of all we are value-driven to create impact from our data solutions. If you have an inherent curiosity about the fields Data and AI, a relevant background and an internal zeal and drive to contribute, please come join us!

Key Responsibilities (What You Will Do)

• Deploy and monitor AI / ML models in production using CI/CD and MLOps best practices (e.g., retraining pipelines, model versioning, rollback strategies)
• Design and maintain scalable and secure MLOps infrastructure, leveraging our cloud platform (GCP), containerization (Docker, Kubernetes), and orchestration tools (Airflow, Kubeflow, Vertex AI, or similar)
• Collaborate closely with Data Scientists, ML Engineers, and DevOps to build end-to-end ML systems - from experimentation to monitoring
• Implement automated testing, performance monitoring, alerting and drift detection pipelines for deployed models
• Ensure governance, traceability, and compliance of ML assets (e.g., model registry, audit trails, data lineage)
• Support AI governance and contribute to enforcing policies and controls across model lifecycle stages
• Identify bottlenecks, propose optimizations, and drive platform-wide observability and scalability efforts
• Contribute to documentation, onboarding materials, and internal tooling for self-service ML infrastructure

Key Requirements

• 5+ years of experience in MLOps, DevOps for AI/ML, Machine Learning Engineering
• Clear knowledge of the end-to-end ML Lifecycle
• Strong programming skills in Python and familiarity with common ML libraries (e.g. scikit-learn, TensorFlow, PyTorch)
• Knowledge and experience with Infracture-as-Code (IaC), and ideally Terraform
• Proficiency in containerization (Docker)
• Experience with cloud platforms (GCP much preferred, or AWS/Azure) and their ML related services (e.g. for GCP: Vertex AI, Cloud Run, Kubernetes Engine, BigQuery)
• Experience with CI/CD tools (e.g. GitHub Actions)
• Good understanding of data pipelines, feature stores, monitoring and alerting tools

Nice to Have Skills

• Experience with SQL
• Experience with Prometheus, Grafana, or SonarQube