Senior Cloud Machine Learning Architect

 

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 & modernization, cloud native application development, DevOps, data engineering, security & compliance and everything in between. At Caylent, our people always come first. 

We are a fully remote global company 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!

 

About this job:

At Caylent, a Senior Cloud Machine Learning Architect works as an integral part of a cross-functional delivery team to design and document machine learning solutions on the AWS cloud for our customers.  We are looking for someone that has a strong understanding of the various model types and tools, and can help our customers connect their business goals with the details of feature design, model training and inference.  You will decompose business goals into architectures that can meet those goals, and guide engineers in their hands-on implementation work.

 

You will participate in daily standup meetings with your team and bi-weekly agile ceremonies with the customer.  Your manager will have a weekly 1:1 with you to help guide you in your career and make the most of your time at Caylent.

Responsibilities

• Work with a team to deliver machine learning solutions on AWS for customers
• Participate in daily standup meetings and in bi-weekly agile ceremonies
• Design and document ML models and data flows on AWS
• Preparation of training data for models

Requirements

• Experience with an IaC tool such as CloudFormation, Amazon CDK or Terraform
• Strong Experience with Amazon Sagemaker and ML libraries such as Tensorflow, MXNet, PyTorch or Scikit-learn
• Strong Understanding of fundamental ML concepts such as feature definition, model meta-data, hyperparameter tuning, stochastic gradient descent, deep learning layer types and activation functions
• Familiarity with MLOps tools such as MLflow, Neptune or Comet is a plus
• Excellent written and verbal communication skills

Benefits 

• Pay in USD
• 100% remote work, either from home or in a shared workspace
• Generous holidays and unlimited flexible PTO
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Individual professional development plan
• Work with an amazing worldwide team and in an incredible corporate culture