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About This Role
Overview:
Old National Bank has been serving clients and communities since 1834. With over $70 billion in total assets, we are a regional powerhouse deeply rooted in the communities we serve. As a trusted partner, we thrive on helping our clients achieve their goals and dreams, and we are committed to social responsibility and investing in our communities through volunteering and charitable giving. We continually seek highly motivated and talented individuals as our people are critical to our success. In return, we offer competitive compensation with our salary and incentive program, in addition to medical, dental, and vision insurance. 401K, continuing education opportunities and an employee assistance program are also included in our benefit suite. Old National also offers a variety of Impact Network Groups led by team members who are passionate about driving engagement, creating awareness of diverse backgrounds and experiences, and building inclusion across the organization. We offer a unique opportunity to join a growing, community and client-focused company that is firmly rooted in its core values. Responsibilities:
Serves as the operational leader of the AI Center of Excellence (AI CoE) governance function within the Transformation Office, responsible for administering the enterprise AI Governance Program across the full AI lifecycle. This role owns the day-to-day execution of AI classification, capability assessment, inventory management, and governance committee facilitation — ensuring the Bank’s use of AI is governed proportionate to risk, aligned with regulatory expectations, and visible to stakeholders at all levels. The individual administers governance reviews across all AI capability types, including third-party vendor-provided services, in-house developed solutions, and enterprise platform systems. This role partners closely with internal risk functions — including Information Security, Data Governance, Model Risk Management, Third-Party Risk Management, and Compliance — to ensure AI governance aligns with enterprise risk expectations. The role requires a blend of AI and technology fluency, risk management acumen, and the ability to drive cross-functional governance processes with rigor and efficiency. Salary Range The salary range for this position is $100,400/Yr. - $203,000/Yr.. The base salary indicated for this position reflects the compensation range applicable to all levels of the role across the United States. Actual salary offers within this range may vary based on a number of factors, including the specific responsibilities of the position, the candidate’s relevant skills and professional experience, educational qualifications, and geographic location. Key Accountabilities* Administer the AI classification process for all AI-involved intake requests across the Bank’s tiered classification framework, including Vendor Embedded AI, AI Platform Services, AI Platform Systems, Platform System Use Cases, and Fourth-Party AI Tools
- Conduct capability assessments aligned to NIST AI 600-1 across 15 GenAI capabilities mapped to cybersecurity core functions (Govern, Identify, Protect, Detect, Respond/Recover) to evaluate risk profile and governance readiness
- Evaluate inherent risk for Platform System Use Cases across five risk factors: data sensitivity, user audience, decision impact, consequence of error, and regulatory scope
- Administer governance reviews for third-party vendor-provided AI services, in-house developed AI solutions, and enterprise AI platform systems — applying the appropriate assessment depth and approval path for each classification tier
- Prepare assessment scorecards, decision recommendations (YES / NO / CONDITIONAL YES), and supporting materials for the AI Advisory Council and AI Oversight Committee
- Track conditional approvals through completion and ensure pre-deployment requirements are satisfied before granting final authorization for Platform System Use Cases
- Maintain the formal Enterprise AI Inventory as the authoritative record of all AI capabilities, including ownership, governance status, risk profile, and technical details
- Conduct reconciliation between the AI Inventory and Application Portfolio Management (APM) records at key lifecycle stages, engaging Business and IT Owners to resolve discrepancies
- Coordinate with the enterprise Change Initiatives Program (CiPT) and Enterprise Architecture team to ensure AI-involved requests are identified and routed appropriately
- Partner with Business Owners and IT Owners throughout classification and assessment to solicit and review all necessary capability documentation
- Coordinate with internal risk partners — Information Security, Data Governance, Model Risk Management, Third-Party Risk Management, and Compliance — to ensure AI governance aligns with enterprise risk expectations and Second Line of Defense standards
- Facilitate material change re-assessments when scope, data, model, or audience changes are identified, and document rationale for re-approval determinations
- Develop and maintain assessment methodologies, templates, scoring frameworks, and supporting governance documentation
- Maintain the AI OneStop SharePoint site as the central resource for approved AI capabilities, acceptable use guidance, governance expectations, and enablement content
- Update the published list of approved AI capabilities monthly and provide instructions for team members to report instances of unapproved AI in use
- Support the annual review and update of the AI Governance Program in coordination with the AI Oversight Committee
- Ensure all AI governance activities operate within the Bank’s risk appetite and align with applicable regulatory requirements
- Support regulatory examination and audit readiness by maintaining defensible records of AI use, governance decisions, and risk assessments
Key Competencies for Position* Risk & Governance Acumen – Deep understanding of risk management frameworks, regulatory expectations, and governance structures within financial services; ability to apply governance rigor proportionate to the risk profile of each AI capability
- Strategic and Analytical Thinking – Applies enterprise-level perspective and data-driven insight to evaluate complex AI capabilities, assess risk, and make sound governance recommendations
- Promotes Change – Champions responsible AI adoption while maintaining governance discipline; enables innovation by ensuring governance facilitates rather than impedes responsible use
- Makes Decisions & Solves Problems – Applies analytical rigor to classification decisions, risk assessments, conditional approval tracking, and governance process improvements
- Compelling Communication – Translates technical AI concepts and risk findings into clear, actionable recommendations for governance committees, senior leadership, and cross-functional stakeholders
- Cross-Functional Collaboration – Navigates effectively across technology, risk, compliance, and business teams to drive governance outcomes and maintain alignment with Second Line of Defense expectations
- Personifies ONB Culture – Demonstrates curiosity, accountability, integrity, and a commitment to responsible innovation
Qualifications and Education Requirements Education* Bachelor’s degree in Technology, Computer Science, Information Systems, Risk Management, Business, or related field (required)
Professional Experience* 5+ years of progressive experience in AI/technology governance, technology risk management, IT audit, or a related discipline within a regulated industry
- 3+ years of direct experience working with AI or machine learning technologies — including implementation, oversight, risk assessment, or governance of AI systems
- 2+ years of experience with a risk management, compliance, or governance function — such as model risk management, technology risk, information security, or regulatory compliance
- Demonstrated ability to develop and administer governance frameworks, assessment methodologies, or control programs
- Proven experience preparing materials for and presenting to governance committees or senior leadership
- Experience coordinating across multiple stakeholder groups (business, technology, risk, compliance) to drive governance outcomes
Technical and Domain Expertise (Required)* Strong understanding of AI and machine learning concepts, including generative AI, large language models, foundation models, and conversational AI
- Ability to evaluate AI capabilities across dimensions of data sensitivity, decision impact, regulatory scope, and technical architecture
- Understanding of the three lines of defense model and how AI governance integrates with enterprise risk management
- Proficiency in managing inventories, tracking governance workflows, and maintaining audit-ready documentation
- Strong analytical skills to evaluate business processes, risk profiles, and translate findings into governance recommendations
Technical and Domain Expertise (Preferred – May Develop on the Job)* Working knowledge of AI risk frameworks (e.g., NIST AI RMF, NIST AI 600-1) and their application to enterprise governance
- Familiarity with AI platforms such as AWS Bedrock, Microsoft Copilot, or similar enterprise AI infrastructure
- Experience with model risk management practices and regulatory expectations for AI in banking
- Knowledge of relevant banking regulations (GLBA, Fair Lending, BSA-AML, ECOA) as they relate to AI use
- Experience with governance, risk, and compliance (GRC) tools and processes
Knowledge to be Developed in Role The following areas represent knowledge the individual will be expected to develop through onboarding, mentorship, and hands-on experience to perform effectively in this role:* AI Monitoring Practices – Develop proficiency in post-deployment observability and monitoring methodologies for AI capabilities, including performance monitoring, drift detection, output quality evaluation, and compliance adherence tracking as the Bank’s monitoring framework matures
- Old National AI Governance Program – Gain deep working knowledge of the Bank’s AI Governance Program, including the classification framework, capability assessment methodology, lifecycle stages, approval workflows, and inventory management processes
- NIST AI 600-1 Assessment Framework – Build applied expertise in the 15 GenAI capabilities mapped to cybersecurity core functions (Govern, Identify, Protect, Detect, Respond/Recover) used in the Bank’s capability assessment methodology
- Internal Risk Partner Processes – Develop working relationships and process knowledge with each internal risk partner function, including Information Security and Technology Risk Management (ISTRM), Data Governance, Model Risk Management (MRM), Third-Party Risk Management (TPRM), and Compliance, to understand their standards, review expectations, and how AI governance intersects with each
- Enterprise Governance Ecosystem – Learn how the AI Governance Program integrates with the Change Initiatives Program (CiPT), Software Development Lifecycle (SDLC) Program, Application Portfolio Management (APM) Program, and other enterprise governance processes
- AI Oversight Committee & Advisory Council Operations – Develop fluency in committee cadence, materials preparation, decision documentation, and escalation protocols to effectively support governance decisioning
- Platform System Architecture – Build understanding of the Bank’s AI platform hierarchy (Platform, Product, Use Case) and how governance operates at each level, including the technical architecture of platforms such as AWS Bedrock
- Regulatory Landscape for AI in Banking – Develop awareness of evolving regulatory expectations for AI in financial services, including guidance from banking regulators and how it translates to governance program requirements
- Incident and Issue Management – Learn the Bank’s incident response and issues management processes as they relate to AI capabilities, including escalation paths and coordination with the IT Risk Office
Key Measures of Success/Key Deliverables:* Maintain a current, accurate, and audit-ready Enterprise AI Inventory with full lifecycle tracking for all AI capabilities
- Complete classification and capability assessments within established SLAs, enabling timely AI Oversight Committee decisioning
- Ensure all deployed AI capabilities have completed the governance lifecycle with appropriate approval
- Deliver clear, well-supported assessment scorecards and risk recommendations that enable informed committee decisions
- Maintain alignment between the AI Inventory and APM records with discrepancies resolved within defined timeframes
- Support successful regulatory examinations and internal audits related to AI governance with complete, defensible documentation
- Continuously improve governance methodologies, templates, and processes based on lessons learned and evolving regulatory expectations
- Maintain strong working relationships with internal risk partners to ensure AI governance remains aligned with enterprise risk expectations
Old National is proud to be an equal opportunity employer focused on fostering an inclusive workplace and committed to hiring a workforce comprised of diverse backgrounds, cultures and thinking styles.
As such, all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, protected veteran status, status as a qualified individual with disability, sexual orientation, gender identity or any other characteristic protected by law. We do not accept resumes from external staffing agencies or independent recruiters for any of our openings unless we have an agreement signed by the Director of Talent Acquisition, SVP, to fill a specific position. Our culture is firmly rooted in our core values.
We are optimistic. We are collaborative. We are inclusive. We are agile. We are ethical.
We are Old National Bank. Join our team!
Salary Context
This $100K-$203K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Old National 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($151K) sits 28% below the category median. Disclosed range: $100K to $203K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Old National Bank AI Hiring
Old National Bank has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Saint Paul, MN, US, Evansville, IN, US. Compensation range: $123K - $203K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
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