Tech Skills
AWS Sagemaker
Amazon Neptune
AWS
ML Ops Processes
Expectations
Be a thought leader in the ML Ops space. Be able to design solutions and plans for the team to execute and follow on.
Provide hands-on development of solutions in the ML Ops space to improve experience for Data Scientists and Machine Learning engineers.
Configure, manage, and improve USAA's AI/ML environment across different cloud platforms.
Qualifications
Hands-on experience in setting up AWS security groups, 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 scientist 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
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 enabling 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
