Sr. Machine Learning Engineer

Sr. Machince Learning Engineer

Location: Cincinnati OH (Hybrid - 3 Days Onsite)

Duration: 1 Year

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

• 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 TensorFlow, PyTorch, Scikit-learn, LangChain, Semantic Kernel, or equivalent technologies.
• Strong communication, problem-solving, and stakeholder management skills.