Machine Learning Engineer

Job Title: ML Ops Engineer
Location: Downtown Cincinnati
Industry: Banking & Financial Services
Years of Experience: 3+ years

TOP SKILLS:
AWS Sagemaker
Python
SQL
MLOps

What You’ll Do
Join our Data Science Enablement squad as a Senior Machine Learning Engineer. You will
use an existing batch inference model to establish a secure, automated deployment pipeline.
This role involves both engineering and change management, including architecture and
training, with a focus on educating data scientists and other Data Science Enablement
members on MLOps. Once the foundational deployment framework is in place, you will
enable additional MLOps capabilities such as MLFlow, A/B testing, real-time endpoints, and
further automation with Model Risk Management (MRM).

Key Responsibilities:
• Develop and implement a secure, automated deployment pipeline.
• Educate and mentor team members on MLOps practices.
• Balance engineering tasks with change management and training.
• Enhance MLOps capabilities with advanced tools and techniques.

Preferred Experience:
• Experience in highly regulated industries like banking, finance, or healthcare.
Qualifications:

Experience:
• Minimum of 3-5+ years of experience in machine learning and MLOps.
• Proven experience with AWS Sagemaker and building end-to-end machine
learning models.
• Experience with data integration and management using IBM DB2 and Snowflake
(or like databases)
• Strong understanding of CI/CD pipelines and automation tools.
Technical Skills:
• Proficiency in programming languages such as Python, R, SQL and/or Java.
• Use of Fifth Third standard DevOps tools such as Jira, Terraform, GitHub, Jenkins
• Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).