Must Have Amazon SageMaker CI/CD Dev Op tools like Jira, Terraform, GitHub, or Jenkins Docker Experience in a highly regulated industry such as Banking/Financial/Healthcare GitHub Python SQL Terraform Nice To Have DBT Java Snowflake JOB SUMMARY Join our Data Science Enablem… JOB DESCRIPTION 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). Job Type: Full-time Pay: $120,000.00 - $130,000.00 per year Schedule: 8 hour shift Experience: Docker: 7 years (Preferred) GitHub: 7 years (Preferred) Python, R, SQL and/or Java.: 6 years (Preferred) Machine learning: 10 years (Preferred) AWS Sagemaker: 10 years (Preferred) Work Location: In person