Senior Machine Learning Engineer - Not an Active Opening, Building Talent Pipeline

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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 At Caylent, a Senior Machine Learning Engineer 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 develop solutions designed by an architect. 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. Your Assignments Work with a team to deliver machine learning solutions on AWS for customers Participate in and contribute to daily standup meetings Develop and implement ML models, MLOps, and analytics Big data processing and preparation of training data for models Your Qualifications Strong experience in building ML models for real world applications Strong experience in at least one of these: AWS ML Services/SageMaker ML libraries like Keras, Tensorflow, PyTorch, Scikit-learn MLOps tools such as MLflow, Kubeflow, Airflow Advanced analytics using time series forecasting and/or inferential statistics Strong experience in one or more of these data processing solutions: Big data processing platforms like Spark, Hadoop, or streaming platforms Data processing and cleansing using Python/Pandas, PySpark, Scala, SQL Strong understanding of feature definition, model meta-data, hyperparameter tuning, stochastic gradient descent, deep learning layer types and activation functions Experience in visualization using SageMaker, ggplot, matplotlib, or seaborn Experience with an IaC tool such as CloudFormation, Amazon CDK or Terraform Excellent written and verbal communication skills

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