Architect and/or review technical solutions for the deployment of AI models, along with the data pipelines and visualization interfaces
Utilize DevOps tools and automate model deployment / tuning via orchestration
Provide guidance for maintenance, support and enhancements to model deployment platforms
Manage MLOps Engineer whom will:
Analyze, design and develop test for model deployment and automation
Design and implement API interfaces
Set up Continuous Integration / Continuous Deployment pipeline
Work with Data Scientists on understanding and enhancing data models for deployment
Liaise with Data Engineers on the requirements for each data sources, understand the ETL required
Work with DBA to manage the DB servers
Work with Cloud infra engineers for on-boarding to AWS and MS Azure
Requirements / Qualifications : -
Practical experience of software engineering or software development experience in making AI/ML models production ready
Degree and equivalent training (e.g. specialist diploma, professional certificate) in Business Analytics, Computer Science / Computer Engineering, Computer Engineering, Information Systems,
Mathematics, Statistics, Engineering or related disciplines that possesses an analytical component
Understanding of programming language such as Python, R and SQL
Foundational knowledge of Cloud computing and infrastructure setup
Foundational knowledge of data pipeline, data engineering and data pre-processing
Foundational knowledge of data visualization tools such as Tableau, QlikSense, Power BI
Agile and Scrum experience is preferred
At least 4 - 6 years\xe2\x80\x99 experience in developing, implementing and maintaining IT systems.