Senior Machine Learning Engineer

Location: Cincinnati OH (3days onsite)We are seeking an experienced Machine Learning Engineer with a strong bias for action, an ownership mindset, and a passion for solving complex business problems through automation and AI. The ideal candidate demonstrates technical excellence, leads by example, and has proven experience delivering enterprise-grade machine learning and Generative AI solutions in regulated environments.

Key Responsibilities: Design, develop, deploy, and maintain scalable machine learning and Generative AI solutions with a focus on reliability, performance, security, and business value. Champion an automation-first approach to software and AI engineering, identifying opportunities to improve operational efficiency and reduce manual processes. Build and operationalize machine learning models and AI-enabled applications throughout the entire model lifecycle, from experimentation to production deployment and monitoring. Develop and deploy Generative AI applications in production environments, preferably within financial services or other highly regulated industries. Apply and advocate Responsible AI principles, ensuring solutions meet requirements for fairness, explainability, transparency, privacy, security, and compliance. Perform model risk evaluations, complete required governance documentation and questionnaires, and partner with stakeholders to address and remediate identified risks. Establish and maintain frameworks for MLOps, model lifecycle management, monitoring, validation, version control, auditability, and AI governance. Collaborate with Risk, Compliance, Information Security, and business partners to ensure machine learning solutions meet enterprise and regulatory standards. Implement CI/CD pipelines, automated testing, model monitoring, observability, and production support processes for machine learning applications. Evaluate emerging machine learning and AI technologies and recommend appropriate adoption strategies. Mentor team members on best practices in machine learning engineering, MLOps, Responsible AI, and production AI systems.

Required Qualifications: Experience level will be 8-12 years Extensive experience designing, developing, and deploying machine learning solutions in production environments. Hands-on experience developing and deploying Generative AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and modern AI development frameworks. Strong understanding of machine learning model development, feature engineering, model evaluation, performance optimization, and model monitoring. Experience conducting model risk assessments and supporting governance, compliance, and validation requirements within regulated environments. Practical experience implementing MLOps practices including model deployment, versioning, monitoring, automated retraining, and CI/CD pipelines. Strong understanding of Responsible AI, model explainability, governance, and risk management concepts. Proficiency in Python and modern machine learning ecosystems, including frameworks such as Tensor Flow, PyTorch, Scikit-learn, Lang Chain, Semantic Kernel, or equivalent technologies. Strong communication, problem-solving, and stakeholder management skills.