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Machine Learning Engineer

Caylent logo

Location
Mexico
Caylent

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 & 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!

The Mission

At Caylent, a 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 also have a weekly 1:1 with your manager 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

  • At least 3 years of hands on experience in at least a few of these ML tools/techniques:
      • Build ML models in SageMaker
      • Build ML models in frameworks like Tensorflow & PyTorch and deploy in SageMaker
      • Train and deploy AWS pre-trained AI Services and Foundational Models
      • Build and optimize models using feature definition, activation functions, hyperparameter tuning and other techniques
      • Integrate ML models into real-time applications and batch workflows, recommend better infrastructure design and optimization
      • Monitor, evaluate and continuously improve model performance, as well as automate these tasks using one or more tools for MLOps
  • Hands on experience in these data engineering tools/techniques:
      • Data integration, cleansing, transformation, and visualization using Python packages, SQL etc.
      • AWS services such as Glue, EMR, Athena, DynamoDB, StepFunctions, EKS etc.
  • Experience with an IaC tool such as CloudFormation, CDK or Terraform
  • Excellent written and verbal communication skills

Benefits

  • 100% remote work
  • Medical Insurance for you and eligible dependents
  • Generous holidays and flexible PTO
  • Competitive phantom equity
  • Paid for exams and certifications
  • Peer bonus awards
  • State of the art laptop and tools
  • Equipment & Office Stipend
  • Individual professional development plan
  • Annual stipend for Learning and Development
  • Work with an amazing worldwide team and in an incredible corporate culture

Caylent is a place where everyone belongs. We celebrate diversity and are committed to creating an inclusive environment for all employees. Our approach helps us to build a winning team that represents a variety of backgrounds, perspectives, and abilities. So, regardless of how your diversity expresses itself, you can find a home here at Caylent.

We are proud to be an equal opportunity employer. We prohibit discrimination and harassment of any kind based on race, color, religion, national origin, sex (including pregnancy), sexual orientation, gender identity, gender expression, age, veteran status, genetic information, disability, or other applicable legally protected characteristics. If you would like to request an accommodation due to a disability, please contact us at [email protected].

Advice from our career coach

As a Machine Learning Engineer at Caylent, you will be an essential part of designing and documenting machine learning solutions on AWS for clients. To stand out as a successful applicant, you should have hands-on experience with ML tools/techniques such as SageMaker, Tensorflow, PyTorch, and AWS pre-trained AI Services. Additionally, expertise in data engineering tools like Python packages, SQL, and AWS services (e.g., Glue, EMR, Athena) is crucial. Here are some specific tips to help you shine as an applicant:

  • Demonstrate at least 3 years of hands-on experience with relevant ML tools/techniques and data engineering tools
  • Highlight your experience in developing and implementing ML models, MLOps, and analytics
  • Showcase your ability to integrate ML models into real-time applications and batch workflows
  • Emphasize your skills in monitoring, evaluating, and continuously improving model performance, as well as automating tasks with MLOps tools
  • Explain your experience with IaC tools like CloudFormation, CDK, or Terraform
  • Ensure your communication skills, both written and verbal, are top-notch

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