Baxter Planning

MLOps Engineer

Remote - Full Time

Company Overview

Founded in 1993, Baxter Planning has 30+ years of industry expertise setting the standard for SaaS in the service supply chain planning space. With a strong and growing customer base, we are developing new products and solutions as well as finding new ways to extend and enhance our established products building on our success in the market. We combine the agility and innovation of a start-up with the stability of an established, profitable, global company.

As a Machine Learning Engineer for Baxter Planning, you will be responsible for implementing and supporting Machine Learning lifecycles, and dealing with production problems. We are looking for an individual who possesses strong technical skills, problem-solving abilities, and a passion for data engineering, with a specific emphasis on AWS. Interested? Join us!

Responsibilities:
  • Work with data scientists to develop, deploy and monitor ML models.
  • Build and manage serverless machine learning infrastructure using AWS services like Lambda, SageMaker, and AWS Step Functions.
  • Implement MLOps platform to automate model lifecycle (develop, monitor, tuning and deploy of ML solutions)
  • Implement Infrastructure-as-code using AWS CloudFormation for reproducibility and scalability
  • Identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems

Requirements:
  • 5+ years of experience in MLOps or Data Science, with a focus on scalable machine learning solutions.
  • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
  • Proficiency in Python and familiarity with machine learning libraries such as TensorFlow, PyTorch, and Scikit-learn
  • Deep understanding of AWS services relevant to MLOps, including SageMaker, Lambda, S3, EC2, and CloudWatch.
  • Expertise in containerization and orchestration using Docker
  • Knowledge of infrastructure-as-code using AWS CloudFormation
  • Deep understanding of software development lifecycle and maintenance
  • Extensive experience with one or more orchestration tools (e.g Airflow, StepFunction)
  • Strong understanding of software engineering best practices and agile methodologies
  • Strong understanding of data structures, algorithms, and machine learning techniques
  • Strong analytical and problem-solving skills, with the ability to handle complex challenges
  • Excellent communication and collaboration skills, capable of working effectively in cross-functional teams
Apply: MLOps Engineer
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