Machine Learning Engineer
2 months ago
Role: MLops Engineer
Experience level: 5-7years
Location: Chennai (Hybrid)
Crayon Data is a leading provider of AI-led revenue acceleration solutions, headquartered in Singapore with a local presence in India and the UAE. The company was founded in 2012 with the vision of simplifying the world’s choices. Our flagship platform, maya.ai, helps enterprises across the Banking, Fintech, and Travel industries create and capture sustainable revenue streams by unlocking the value of data.
maya.ai's capability is driven by four “as a Service” components - Data, Recommendation, Customer Experience, and Marketplace - that work individually and together to create tangible results. Crayon Data recently won the E50 awards organized by KPMG and the Business Times in Singapore. Crayon was featured in HFS Hot Vendors Compendium in 2021. They were also among the top 15 finalists at Emerging Enterprise Awards 2019, Singapore.
Can you say “Yes, I have” to the following?
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
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