AI Engineer - Automated Channels Transformation

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 designs and builds artificial intelligence systems that address complex business challenges and deliver measurable value with a focus on agentic AI . This role functions as a forward-deployed enginee r partnering with product owners, line s -of-business, engineering, security, risk, and compliance to identify high-impact opportunities , and deliver safe and effective agentic AI solutions .   This role focuses on AI for the Customer by using generative A I predominantly in AWS to deliver operational efficiency and superior customer experience . The Principal AI Engineer develops AI agents that leverage reasoning, planning, retrieval, and tools to complete complex tasks; establishes evaluation and documentation practices; monitors production performance; and produces evidence needed for governance.   This role communicates complex AI concepts, risks, opportunities, and recommendations clearly to technical teams, business stakeholders, and senior leaders while supporting the Bank’s business goals and remaining within risk appetite.   Essential Duties and Responsibilities   AI Development and Implementation   Design and build generative AI and /or agentic systems that solve business problems and deliver measurable value   Develop AI agents that use reasoning, planning, retrieval, and tool integration to complete complex tasks   Evaluate AI agents before and after their deployment to drive continuous improvement and ensure effectiveness of AI systems   Research, prototype, and apply emerging AI models, agent frameworks, orchestration patterns, and enterprise development practices    Analytics and Insights   Design and execute experiments to evaluate prompts, retrieval strategies, agent workflows , tool integrations, and user experience   Conduct data analysis to compare prompts, workflows, and tools across runtime, accuracy, and output quality; report results for governance processes; and quantify return on investment for AI solutions   Use supervised and unsupervised learning techniques to understand customer needs on the channels we support to inform decision making.   Define success metrics for each AI use case, such as containment, time to resolution, customer satisfaction, or sentiment.   Monitor agent performance in production, including latency, failure rates, tool-call success, escalation rates, retry behavior, and cost per interaction   Produce evidence for AI governance reviews, including evaluation results, control effectiveness, model/tool performance, and risk mitigation outcomes   Collaboration and Communication   Deliver complex AI projects and cross-team initiatives with limited supervision, ensuring solutions are integrated, scalable, and compliant   Serve as a technical authority for colleagues by influencing architectural decisions, and shaping engineering standards across products and platforms   Partner with product owners and line-of-business teams to understand day-to-day workflows, identify automation opportunities, and translate business needs into well-defined AI and agentic use cases with clear objectives, constraints, and success criteria   Educate technical teams, business stakeholders, and senior leaders on AI concepts, solution designs, recommendations , opportunities, and emerging trends through clear, practical guidance     Collaborate with software engineers, cloud engineers, data engineers, and data scientists to integrate AI solutions into existing systems and products   Work with compliance, security, and risk teams to ensure AI systems meet governance, regulatory, and responsible AI requirements before and after deployment   Minimum Knowledge, Skills and Abilities Required   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 software or AI solutions in production environments    Technical Skills   Strong programming skills in Python, Typescript, Java, Go , or similar languages used for AI engineering and software development   Expertise in generative AI techniques , e.g. context engineering, LLM fine-tuning, retrieval-augmented generation (RAG), evaluation frameworks, model context protocol (MCP) , loop/graph engineering, skills/open knowledge format, and agentic system design   Experience with cloud computing platforms , preferred (especially AWS)   Familiarity with prompt optimization, retrieval tuning, caching, and model selection to improve generative AI performance, latency, cost, and reliability   Knowledge of agent frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, Strands, Google ADK , A nthropic sdk, or O pen AI sdk   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 preferred   Business and Soft Skills   Demonstrates curiosity and adaptability by quickly learning new skills, techniques, tools, and frameworks as AI technologies and business needs evolve.   Communicates clearly, builds consensus, and presents ideas effectively in written and verbal formats.   Promotes knowledge sharing and supports strong cross-team collaboration.   Uses sound analytical judgment to solve problems, prioritize competing demands, and work independently.   Works well in Agile environments and with standard business productivity tools.   Understan