Principal AI Engineer - Automated Channel

Fifth Third Bank•Cincinnati, OH

About The Position

Make banking a Fifth Third better® We connect great people to great opportunities. Are you ready to take the next step? Discover a career in banking at Fifth Third Bank. GENERAL FUNCTION: The Principal AI Engineer architects and implements artificial intelligence and machine learning systems that address diverse business challenges throughout the Bank, with a strong focus on research and experimentation of generative AI models and agentic systems. This role involves conducting rigorous data analysis, performing statistical evaluation, designing experimental frameworks, and developing algorithms that effectively leverage both structured and unstructured data. The AI Engineer creates solutions that range from on-demand analytics to fully integrated software systems, working closely with cross-functional teams to align technical solutions with business requirements. Staying apprised and educating senior leaders on emerging AI trends, risks, and opportunities. Responsible and accountable for risk by openly exchanging ideas and opinions, elevating concerns, and personally following policies and procedures as defined. Accountable for always doing the right thing for customers and colleagues and ensures that actions and behaviors drive a positive customer experience. While operating within the Bank's risk appetite, achieves results by consistently identifying, assessing, managing, monitoring, and reporting risks of all types.

Requirements

Education and Experience Bachelor's degree in Computer Science, Statistics, Data Science, Mathematics, or related technical field; Advanced degree preferred but not required.
6+ years of experience developing and deploying machine learning or AI solutions in production environments.
Technical Skills Strong programming skills with proficiency in Python; familiarity with JavaScript and SQL.
Expertise in generative AI techniques including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), evaluation frameworks, tool integration, and agentic system design.
Experience with cloud computing platforms (AWS preferred, specifically Bedrock, Sagemaker, Lex, etc.).
Skilled in data visualization and storytelling, effectively communicating complex analytical insights in clear, actionable formats for technical and non-technical audiences.
Experience with machine learning frameworks (PyTorch, scikit-learn, Hugging Face).
Practical knowledge of deep learning, neural networks, and traditional ML algorithms.
Familiarity with model optimization techniques including quantization and distillation.
Knowledge of AI orchestration frameworks (LangChain, LlamaIndex, MCPs, etc.)
Engineering Practices Proficiency with version control systems (Git/GitHub)
Understanding of CI/CD pipelines and DevOps practices
Experience with containerization and Infrastructure as Code (Docker, Terraform)
Knowledge of data structures, algorithms, and software design principles
Experience designing observability systems for AI applications
Business and Soft Skills Communicates clearly and builds consensus across teams
Mentors colleagues and promotes knowledge sharing
Excellent written and verbal communication skills
Strong analytical thinking and problem-solving abilities
Ability to manage time effectively and prioritize competing demands
Self-motivated with demonstrated capacity to work independently
Experience working in Agile environments
Proficiency with Microsoft Office suite (Word, Excel, PowerPoint)
Understanding of ethical considerations in AI deployment

Responsibilities

AI Development and Implementation Design, develop, and implement AI and machine learning systems that address specific business challenges and deliver measurable value.
Create and maintain model documentation, ensuring transparency in methodologies and approaches.
Research, test, and apply state-of-the-art generative AI models and/or solutions for potential use.
Develop agentic AI systems capable of autonomous reasoning, planning, and tool utilization for complex task completion.
Create comprehensive evaluation frameworks to assess model performance, detect hallucinations, and ensure output quality.
Stay current with emerging AI/ML technologies, frameworks, and methodologies.
Contribute to establishing best practices for AI development and deployment.
Sets enterprise-wide technical standards by defining reference architectures, chairing design reviews, and approving model lifecycle gates (e.g., data sourcing, bias audits, drift monitoring).
Analytics and Insights Extract meaningful patterns and insights from complex, multi-dimensional data sets.
Apply advanced analytics including predictive modeling, machine learning, and optimization techniques.
Translate business questions into well-defined analytical problems with clear objectives Design and execute experiments with statistically valid methodologies and evaluation criteria.
Develop specialized analytics for banking-specific use cases while maintaining compliance with financial regulations.
Collaboration and Communication Leads projects or processes with limited supervision, applying advanced knowledge to solve complex problems.
Acts as a resource for colleagues, influencing technical direction and standards across multiple products or platforms.
Drives cross-team technical initiatives, ensuring integration, scalability, and compliance across multiple products and platforms.
Partner with cross-functional teams to understand business requirements and translate them into technical solutions.
Effectively communicate complex technical concepts to non-technical stakeholders.
Present findings, recommendations, and insights to business teams in accessible formats.
Collaborate with software engineers, cloud engineers, data engineers, and data scientists to integrate AI solutions into existing systems and products.
Work with compliance and security teams to ensure AI systems meet banking regulatory requirements.