Position : AWS SageMaker Engineer
Location : Cincinnati, OH - Hybrid onsite 2-3 days a week
Pay : $63-$73 / hr
Duration : 12-month contract
REQUIRED SKILLS AND EXPERIENCE :
Experience standing up SageMaker as a new platform Ability to effectively communicate and take the lead on this implementation
JOB DESCRIPTION :
A large financial organization is seeking a AWS SageMaker Engineer that will sit hybrid onsite in Cincinnati Ohio for a long term contract with the possibility of full time hire.
The ideal candidate will have a strong background in machine learning, data science, and cloud computing, with specific experience in deploying and managing models using AWS SageMaker.
Key Responsibilities :
- Deploy machine learning production models using AWS SageMaker.
- Terraform experience
- Experience with security, compliance, and governance of Sagemaker
- Manage and optimize SageMaker instances and resources.
- Collaborate with data scientists and engineers to integrate models into production environments.
- Monitor and maintain deployed models to ensure performance and scalability.
- Implement best practices for model versioning, monitoring, and retraining.
- Troubleshoot and resolve issues related to model deployment and performance.
- Stay up-to-date with the latest developments in AWS SageMaker and related technologies.
Squad outcomes :
- Future (2025 & Beyond) Utilize AWS SageMaker to expand Feature Store, introduce Model Registry, CI / CD, Real-Time models for our large data science credit models.
- The squad is currently working on an in-house build of Feature Store to help speed up modeling process for our Data Science department.
Combination of Snowflake, Cloud Pak for Data. (More on this later)
o Currently, data scientist build model features (attributes) about customers in their own Jupyter notebook that feed into their models and never reusable for others aka reason for Feature Store.
They are also working on building real time scoring framework for our loan / card application process. Right now it’s batch and can be almost 31 days behind.
o Technology used : Docker, Kafka, Snowflake, Feature Store
This is the most important part : They are working on bringing in AWS Sagemaker as a replacement for IBM Cloud Pak for Data.
This is where we deploy our critical production models and where all most of modeling is done at the bank.
o We need someone that has been through standing up AWS SageMaker into their company and / or someone that can deploy models in AWS SageMaker.
o We are in early innings with SageMaker and just scratching the surface. We need help getting this platform stood up
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