Machine Learning Architect - Not an Active Opening, Building Talent Pipeline
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Job Description
Caylent is a cloud native services company that helps organizations bring the best out of their people and technology using Amazon Web Services (AWS).
We provide a full-range of AWS services including workload migrations and modernization, cloud native application development, DevOps, data engineering, security and compliance, and everything in between. At Caylent , our people always come first.
We are a global company and operate fully remote with employees in Canada, the United States, and Latin America. We celebrate the culture of each of our team members and foster a community of technological curiosity. Come talk to us to learn more about what it means to be a Caylien!
Note: This isn’t an active role right now, but we’re building a community of great talent for future opportunities at Caylent . If your background aligns with what we’re looking for, our team may reach out to learn more about you and explore potential future fits.
The Mission We are seeking an exceptional Machine Learning Architect to join our growing Cloud Native Applications team. The right candidate is someone who has deep expertise in ML system design and is passionate about working with our customers, partners, and colleagues to drive innovation forward.
Your mission will be to work alongside Caylent ’s Engineers, Engineering Managers, and Project Managers to deliver AWS solutions across our diverse and forward-thinking customer base. You’ll work with the latest technologies and support customers looking to bring cutting-edge ideas to market.
Your Assignment You will be a mission control specialist, guiding Cayliens and Customers alike through Agile ceremonies like stand-ups, retrospectives, and more. You will translate customer requirements and into a workable backlog of tickets for engineers.
Delegate tickets to a team of engineers in order to complete customer projects. Lead requirements gathering, backlog grooming, and architecture discussions. Apply your understanding of DevOps pipeline