Machine Learning Engineer (Fully Remote)

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).