Direct Client- AWS SageMaker Engineer

Title: AWS SageMaker Engineer

Location: Cincinnati Ohio 

Duration: Sep 16, 2024 - Dec 31, 2024

Rate: $80-85/hr(C2C)

JOB DESCRIPTION

Job Description:
We are seeking an experienced AWS SageMaker Specialist to join our team. 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.

Must Have

• Experience as a lead or key player bringing in AWS Sagemaker as a new "platform"
• Needs to be able communicate clearly and often

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.

Soft Skills:
-Needs to be able to communicate clearly and often

-Need a leader, not a follower

-We want someone who has lead or key player bringing in AWS Sagemaker as a new “platform”
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)
• Currently, data scientist build model features (attributes) about customers in their own Jupyter notebook that feed into their models and never reuseable 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.
• 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.
• We need someone that has been through standing up AWS Sagemaker into their company and/or someone that can deploy models in AWS Sagemaker.
• We are in early innings with Sagemaker and just scratching the surface. We need help getting this platform stood up