Design, build, and maintain scalable and robust infrastructure, platforms, and CI/CD pipelines specifically for the ML lifecycle (training, validation, deployment, monitoring, retraining)
Champion the evaluation, selection, and adoption of cutting-edge MLOps tools, technologies, and best practices to enhance efficiency, reliability, and speed of deployment
Act as the primary technical lead for AI/ML operational concerns, effectively communicating deployment status, model performance metrics, infrastructure needs, and risks to both technical and senior stakeholders
Foster strong collaboration between Data Science, Software Engineering, DevOps, and Infrastructure teams to ensure seamless integration and operation of AI models
Job Offer
The opportunity to play a pivotal role in shaping an AI-forward, data-driven culture in a dynamic and data-ready work environment
Requirements
Master's degree or equivalent practical experience in Computer Science, Engineering, Statistics, or a related quantitative discipline
Minimum of 8 years of combined experience in software engineering, DevOps, data engineering, or data science, with at least 5 years specifically focused on MLOps or building/managing ML systems in production
Strong understanding of the end-to-end machine learning lifecycle and associated challenges in operationalisation
Job Offer
The opportunity to play a pivotal role in shaping an AI-forward, data-driven culture in a dynamic and data-ready work environment
Requirements
Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field
3–5+ years as an MLOps Engineer with a strong focus on operationalising machine learning models
Demonstrable experience in designing, building, and maintaining CI/CD pipelines for ML models
Strong programming skills, particularly in Python
Familiarity with ML libraries/frameworks such as PyTorch, TensorFlow, etc.
