MLops Engineer

Extensive knowledge in machine learning concepts, models and algorithms Good knowledge on Machine Learning Operations or Development Operations Good knowledge on software engineering DevOps Well 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