Machine Learning (Ops) - Engineer

ML Pipeline Development and Automation: Design, build, and maintain end-to-end AI/ML CI/CD pipelines using Azure DevOps and leveraging Azure AI Stack (eg, Azure ML, AI Foundry ) and Dataiku Model Deployment and Monitoring : Deliver tooling to deploy AI/ML products into production, ensuring they meet performance, reliability, and security standards. Implement and maintain a transversal monitoring solutions to track model performance, detect drift, and trigger retraining when necessary Collaboration and Support : Work closely with data scientists, AI/ML engineers, and platform team to ensure seamless integration of products into production. Provide technical support and troubleshooting for AI/ML pipelines and infrastructure, particularly in Azure and Dataiku environments Operational Excellence : Define and implement MLOps best practices with a strong focus on governance, security, and quality, while monitoring performance metrics and cost-efficiency to ensure continuous improvement and delivering optimized, high-quality deployments for Azure AI services and Dataiku Documentation and Reporting : Maintain comprehensive documentation of AI/ML pipelines, and processes, with a focus on Azure AI and Dataiku implementations. Provide regular updates to the AI Platform Lead on system status, risks, and resource needs About you: Proven track record of experience in MLOps, DevOps, or related roles Strong knowledge of machine learning workflows, data analytics, and Azure cloud Hands-on experience with tools and technologies such as Dataiku, Azure ML, Azure AI Services, Docker, Kubernetes, and Terraform Proficiency in programming languages such as Python, with experience in ML and automation libraries (eg, TensorFlow, PyTorch, Azure AI SDK ) Expertise in CI/CD pipeline management and automation tools using Azure DevOps Familiarity with monitoring tools and logging frameworks Catch this opportunity and invest in your skills development, should your profile meet these requirements. Additional attributes: A proactive mindset with a focus on operationalizing AI/ML solutions to drive business value Experience with budget oversight and cost optimization in cloud environments. Knowledge of agile methodologies and software development lifecycle (SDLC). Strong problem-solving skills and attention to detail Work Experience: 3-5 years of experience in MLOps Minimum Education: Advanced degree (masters or PhD preferred) in Computer Science, Data Science, Engineering, or a related field