Senior Engineer I - Machine Learning

CDW

1d•Remote

About The Position

At CDW, we make it happen, together. Trust, connection, and commitment are at the heart of how we work together to deliver for our customers. It’s why we’re coworkers, not just employees. Coworkers who genuinely believe in supporting our customers and one another. We collectively forge our path forward with a level of commitment that speaks to who we are and where we’re headed. We’re proud to share our story and Make Amazing Happen at CDW. As a Engineer, Machine Learning you will work with our AI Solutions team to design, develop, build MLOps workflows and help deploy machine learning models to solve complex business problems. You will help our customers build modern data solutions on the AWS stack. This position is 100% remote with up to 25% travel required.

Requirements

Design & implementation experience with distributed applications
Experience in database architectures and data/MLOps pipeline development
Ability to work with loading and extracting data from Glue, SQL, DDL, DML commands
Working knowledge of AWS data technologies, like Sagemake MLFlow or Sagemaker MLOps experience
Working knowledge of machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, or similar
Python or R experience
Working knowledge of AWS cloud computing platforms and services such as Sagemaker, S3, Athena,and Lambda
Working knowledge of software engineering principles and best practices, including version control, testing, and continuous integration/continuous deployment (CI/CD)
Ability to handle unstructured, semi-structured data, working in a data lake environment
Ability to work in an Agile environment
Working knowledge of software development tools and methodologies
Presentation skills with a high degree of comfort speaking with IT management, customers, and developers
AWS Certification (required within 6 months of hire)

Responsibilities

Under the supervision of Big Data Consultants and Architects, work with multiple clients simultaneously to implement enterprise-wide scalable operations on AWS
Deploy and monitor machine learning models on AWS using tools such as SageMaker
Implement machine learning pipelines including data cleaning, training, evaluation and deployment
Develop models from customer data to meet customer goals
Write infrastructure as code scripts in CDK or Terraform to help make service deployment more efficient and consistent
Create data visualizations and reports from the data that has been extracted, transformed and loaded with tool such as Amazon Quicksight
Collaborate with data scientists, data engineers, and product managers to document requirements and deliver machine learning solutions
Develop and maintain data pipelines, feature engineering, and model training and deployment framework
Conduct exploratory data analysis, data preprocessing, and data cleaning
Perform model evaluation, selection, and optimization
Implement and maintain automated testing and monitoring for machine learning models in production