This role is onsite in Cincinnati, OHA little about this gig Our client, a leading financial services organization, is seeking a motivated engineer to help build and enhance a modern AWS-based machine learning platform that empowers data scientists and ML engineers across the business. This is an exciting opportunity for someone eager to gain hands-on experience with cloud infrastructure, automation, and ML operations while supporting a growing, high-impact environment. In this role, you will support core platform components, improve reliability and security, and collaborate closely with teams across the organization to drive platform usability and operational excellence.
Responsibilities:
Build, maintain, and improve AWS infrastructure supporting Amazon SageMaker and related machine learning services.
Develop and maintain Infrastructure as Code (IaC) using Terraform, while supporting CI/CD automation for infrastructure and ML workflows.
Monitor platform reliability, performance, and cost efficiency, troubleshooting infrastructure and deployment issues as they arise.
Collaborate with data scientists and ML engineers to enhance platform usability, and contribute to documentation and operational best practices.
The ideal candidate:
Understanding of AWS fundamentals, including networking, IAM security, EC2/ECS, S3, Lambda, and SQS.Experience developing in Python for scripting, automation, or Lambda-based services.
Hands-on experience with Terraform or other Infrastructure as Code tooling.
Familiarity with CI/CD tools such as Jenkins or GitHub Actions.
Exposure to Linux-based environments and shell scripting.
Strong problem-solving abilities and an eagerness to learn new technologies.
Ability to collaborate effectively with cross-functional teams.
Reasons to love itChance to work directly with cutting-edge machine learning platform technologies such as SageMaker.
Opportunity to develop deeper AWS and infrastructure automation skills in a supportive engineering environment.
High-impact role influencing how ML engineers and data scientists build and deploy models.
Collaborative culture with a strong emphasis on learning, documentation, and continuous improvement.
