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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:
The AI Learning Specialist supports the enterprise AI strategy by designing, delivering, and evaluating learning solutions that build AI fluency, encourage responsible adoption, and enable practical business use cases. This role owns the instructional design lifecycle for AI learning experiences, partners with the Center of Excellence, stakeholders and subject matter experts to ensure learning solutions align with evolving business needs and AI capabilities. The AI Learning Specialist is responsible for delivering quality learning experiences to implement and adopt AI, creating job aids, developing alternate training plans, evaluating the effectiveness of delivery and course design, as well as collaborating with Instructional Designers and line of business subject matter experts to create and update training materials. Periodic travel may be required for this position.
Salary Range The salary range for this position is $61,200/yr - $123,700/yr plus bonus. 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
Drives and supports AI organizational learning strategy* Partners with the Center of Excellence, Executive Leadership Team, IT Leadership, and the Learning Director to support the enterprise AI strategy.
- Builds and delivers strategic training plans to support executive and key leader use of AI for business effectiveness.
- Develops and implements user enablement programs that build AI fluency and help team members apply AI to improve business processes across the organization.
- Collaborates with cross-functional teams to support the successful implementation and scaling of AI solutions in alignment with organizational goals.
- Identifies opportunities to extend and optimize AI tools, including Copilot and Copilot agents within business processes and systems at scale.
- Facilitates learning experiences AI Tools and M365 Copilot adopted by the organization. Training may address the AI ecosystem, system enhancements, basic and advanced functionality, responsible use, and coaching People Leaders to reinforce effective and ethical AI practices.
- Continuously monitors emerging trends and advancements in AI and IT technologies to identify opportunities for innovation and alignment with organizational goals.
- Cultivates AI and IT domain expertise through ongoing research, industry engagement, and professional development to inform learning solution design, stakeholder consultation, and enterprise capability building.
- Prepares for upcoming courses including the coordination of material, system testing, and monitoring of course enrollment.
- Utilizes the leader guide and approved course material to engage participants in the learning process via discussion, effective questioning, activities, and practice.
- Manages the training environment, analyzes results, identifies gaps, coaches’ participants, and provides feedback to their manager as needed.
Design learning solutions* Leads the end-to-end instructional design process by analyzing performance and learning needs, identifying target audiences, defining scope, writing performance-based objectives, and recommending the appropriate learning strategy and delivery methods.
- Designs and develops accessible, performance-focused, interactive learning solutions using instructional design methodologies and adult learning principles. Creates virtual instructor-led, online, video, blended, and performance-support materials, including facilitator and participant guides, storyboards, job aids, reference guides, assessments, practice activities, and reinforcement tools.
- Develops project plans aligning work to AI Strategy and roadmaps with clear deliverables, milestones, dependencies, review cycles, and implementation requirements.
- Develops prototypes and storyboards, facilitate stakeholders and subject matter expert reviews, incorporates feedback, and completes quality assurance to ensure accuracy, accessibility, usability, and alignment with learning objectives.
- Creates assessment and evaluation strategies that measure knowledge, skill application, adoption, and program effectiveness; analyzes results and recommends improvements to stakeholders.
- Maintains and demonstrates in-depth knowledge of regulations, products, process, policy, and procedures for ethical AI usage and partners with line of business leaders to update and communicate learning content when changes occur.
Consults on training effectiveness* Consults with Learning and Development team members, Center of Excellence, subject matter experts, stakeholders, and clients to assess current performance and knowledge levels, identify skill gaps and target audiences, define learning objectives, and recommend effective learning solutions and delivery methods.
- Maintains knowledge of organizational and line of business performance expectations and applies that knowledge to translate business needs into practical, learner-centered solutions.
- Recognizes when changing client, learner, technology, or organizational expectations require a new approach. Recommends alternate solutions and adapts design and delivery strategies accordingly.
Updates course content* Maintains knowledge of applicable regulations, products, processes, policies, procedures, and responsible AI practices, and updates learning content when changes occur.
- Independently creates, revises, and maintains instructor-led, virtual, online, video, and performance-support content; communicates changes with Learning and Development team members and stakeholders; and manages appropriate review and approval cycles.
- Reviews and provides feedback on policy, procedural, system, and technology changes to determine learning impacts. Tests system releases in the training environment prior to scheduled learning experiences when applicable.
Key Competencies for Position Strategy in Action:* Connects work to broader organizational goals and imperatives to drive strategy forward while removing barriers, anticipating and mitigating risks.
- Proactively identifies future trends and enhancements, seeking insight from other sources to generate potential solutions. Iterate based on AI-generated feedback and learner behavior.
Promotes Change:* Proactively recognizes a need and takes accountability for implementing an improvement or change. Fosters change agility within business areas by seeking perspectives, leveraging other’s ideas and recognizing change efforts.
- Accelerates change by creating a safe environment for others to try new things and learn from mistakes driving enhancements to organizational culture, process, products/services.
Culture Leadership:* Communication: Applies active listening skills and skillful use of questions to understand the client's situation, needs, and desired outcome(s); adapts communication style and approach to accommodate individual needs and preferences.
- Collaboration: Seeks, develops, and maintains trusted relationships with others to achieve business goals and objectives; shares knowledge, information, ideas, and suggestions to accomplish mutual goals
Execution Leadership:* Drive and Execution: Committed to achieving established goals, overcoming obstacles, and continuously learning; focuses on ways to succeed by changing strategies, increasing effort, using varying approaches; seeks and leverages opportunities to collaborate with others to achieve results; consistently achieves performance targets.
- Makes Decisions & Solves Problems: With minimal oversight, seeks to identify what caused an issue; incorporates input from multiple sources to ensure effective action and shared ownership; decisions are sound based on what was known at the time and are based on a blend of analysis, wisdom, experience, and judgement.
Qualifications and Education Requirements* Bachelor’s degree in Business, Education, Instructional Design, Learning Technologies, IT, Computer Science, Data Science, or a related field, or equivalent work experience, preferred.
- Experience with AI technology with a focus on the Microsoft suite of tools.
- Knowledge of AI ethics and responsible practices preferred.
- Ability to translate organizational AI strategies into broad and tangible programs and practices.
- Minimum of 3 years of experience in learning and development or instructional design, including developing and delivering instructor-led, virtual, and digital learning solutions, preferred.
- Knowledge of regulations; along with Old National’s products, policy, procedures, processes, and systems preferred.
- Strong critical thinking, analytical and problem-solving skills using sound judgment to implement initiatives.
- Strong attention to detail.
- Exceptional organizational skills with the ability to self-direct, prioritize, manage changing priorities and deadlines.
- Strong coaching, consultation, and collaboration skills to build client relationships.
- Effective oral and written communication skills; ability to explain complex concepts in simplistic terms.
- Demonstrated knowledge of adult learning theory and instructional design methodologies, including needs analysis, audience analysis, scope and design documentation, performance-based objectives, storyboarding, assessments, curriculum development, implementation, and evaluation.
- Demonstrated ability to translate complex AI concepts, business processes, and technical information into clear, practical, learner-centered experiences and resources.
- Experience using e-learning authoring, multimedia, video, and web-based development tools, such as Articulate 360, Captivate, HTML, or comparable solutions, and experience uploading courses and building curricula in a Learning Management System, preferred.
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 $61K-$123K range is in the lower quartile 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($92K) sits 56% below the category median. Disclosed range: $61K to $123K.
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.
Frequently Asked Questions
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