MLops Engineer @ Crayon Data

• Extensive knowledge in machine learning concepts, models and algorithms
• Good knowledge on Machine Learning Operations or Development Operations
• Good knowledge on software engineering DevOps
  we'll versed in major machine learning operations platforms and providers
• Quickly adapt to new platforms and tools

Can you say Yes, I will to the following

• Deliver end-to-end AA & AI model deployment by actively conducting the technical operationalization steps
• Support the use case squads in code QA/QC to be ready for deployment
• Contribute to cross-functional problem-solving sessions with use case squad and provide inputs regarding productization
• Orchestrate production pipeline and model/code transfer to production environment. Ensure it is working as intended
• Ensure the code transferred to production is bug-free, all sources are setup for production servers
• Find run-time performance improvement and decide which ML technologies will be used in production environment
• Monitor model performance, model drift and data drift and take appropriate remedial action to correct model performance
• Collaborate with other stakeholders (BT, EDM, BU) to ensure all infrastructure requirements are completed for model productization
• Document the various approach, techniques used in operationalization for maintainability and knowledge sharing purpose
• Great understanding of different MLOps concepts and platforms
• Mentor and coach MLOps Engineer on MLOps techniques, processes, new platforms, ways of working and supporting in their use case operationalization delivery