MLOps engineer

Responsibilities

• In this role you will help to develop, build, design, continuously improve, and support the MLOps Platform
• You will participate in all stages of development, use various software and tools, and enable self-service tools for data science teams
• The role also involves designing, developing, integrating, and deploying tools for Data Science and Machine Learning (ML) Research
• The successful applicant will design and operate a framework for Machine Learning Operations (MLOps), advise on software engineering for ML, and ensure consistency with cloud architectural principles

Qualifications

• Hands-on experience in setting up AWS security groups, and roles, and implementing best practices around SageMaker and Neptune
• Hands-on experience working with at least one cloud hyperscaler (AWS)
• Ability to build MLOps pipelines on AWS SageMaker + experience with AWS Sagemaker deployment
• Hands-on DevOps experience – CI/CD in Jenkins, Bitbucket, Kubernetes, OpenShift

Preferred:

• Knowledge in machine learning frameworks or libraries such as TensorFlow, Keras, PyTorch, etc
• Ability to understand tools used by data scientists and experience with software development and test automation
• Experience with Agile development and delivery – Scrum, Lean, XP, Kanban methodologies
• Proficiency in modern programming languages such as Python