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About This Role
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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.
Essential Duties and 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.
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 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
Position not available for immigration sponsorship
\#LI\-MB1
Principal AI Engineer \- AI Transformation
At Fifth Third, we understand the importance of recognizing our employees for the role they play in improving the lives of our customers, communities and each other. Our Total Rewards include comprehensive benefits and differentiated compensation offerings to give each employee the opportunity to be their best every day.
The base salary for this position is reflective of the range of salary levels for all roles within this pay grade across the U.S. Individual salaries within this range will vary based on factors such as role, relevant skillset, relevant experience, education and geographic location. In addition to the base salary, this role is eligible to participate in an incentive compensation plan, with any such payment based upon company, line of business and/or individual performance.
Our extensive benefits programs are designed to support the individual needs of our employees and their families, encompassing physical, financial, emotional and social well\-being. You can learn more about those programs on our 53\.com Careers page at: https://www.53\.com/content/fifth\-third/en/careers/benefits.html or by consulting with your talent acquisition partner.
LOCATION \- Cincinnati, Ohio 45202
Attention search firms and staffing agencies: do not submit unsolicited resumes for this posting. Fifth Third does not accept resumes from any agency that does not have an active agreement with Fifth Third. Any unsolicited resumes – no matter how they are submitted – will be considered the property of Fifth Third and Fifth Third will not be responsible for any associated fee.
Fifth Third Bank, National Association is proud to have an engaged and inclusive culture and to promote and ensure equal employment opportunity in all employment decisions regardless of race, color, gender, national origin, religion, age, disability, sexual orientation, gender identity, military status, veteran status or any other legally protected status.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Fifth Third Bank, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Fifth Third Bank AI Hiring
Fifth Third Bank has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Cincinnati, OH, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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