Interested in this AI/ML Engineer role at Union Bank & Trust Co.?
Apply Now →About This Role
Position Summary: The AI Enablement and Software Development Officer leads and manages a software development team while guiding the organization’s AI tool research, evaluation, and implementation efforts. This role is responsible for delivering high-quality software that creates value for the business and customers while advancing AI capabilities through structured research, tool evaluation, and governance execution.
This leader plans, coordinates, and directs activities of the Software Development Team in the design, development, and implementation of applications, leads continuous improvement of Agile practices, and implements traditional and modern applications including APIs, containers, and cloud-native solutions.
In parallel, this role advances the Bank’s AI capabilities by conducting in-depth research on advances in AI functionality, evaluates current and potential AI tools (including those embedded in third-party systems), making informed recommendations, and executes AI governance policies developed in partnership with senior leadership. The Officer partners with IT, Security, Risk, Compliance, and business lines to ensure AI adoption aligns with bank policies and regulatory expectations.
Essential Functions:
- Plan, coordinate, and direct activities of the Software Development Team in the design, development, and implementation of applications.
- Manage all phases of development and maintenance including analysis, planning, development, testing, and documentation.
- Maintain an "AI capability radar" tracking new and emerging AI offerings including embedded features in core platforms, vendor applications, and standalone tools.
- Lead structured evaluations of candidate AI tools and capabilities, emphasizing governance, control, safety, transparency, workflow integration, output quality, security, data residency, and compliance fit.
- Identify and recommend high-value AI opportunities to senior management.
- Develop and maintain an AI enablement strategy and roadmap that translates senior leadership priorities into clear, sequenced initiatives and measurable outcomes.
- Shape and improve AI enablement platforms, resources, and support channels, including tool selection guidance, reference materials, training content, adoption communities, and practical resources that help employees use approved AI tools effectively and responsibly.
- Responsible for various phases of development and maintenance of approved projects including analysis, planning, development, testing and documentation.
- Work closely with the Quality Assurance team to implement Agile QA practices.
- Direct the implementation of traditional and modern applications including APIs and cloud-native solutions.
- Partners with senior level managers to determine project complexity, priority, and other processing requirements.
- Works with project management, engineering, and technical staff to solidify technical and infrastructure requirements.
- Tracks and plans project lifecycles to coordinate and manage issues and risks.
- Works with support and documentation groups to provide user training and manuals for new systems and system enhancements.
- Interviews, hires, and facilitates initial and ongoing training and mentors and completes evaluations of the Software Development Team to ensure an ongoing high level of product knowledge and customer service skills.
- Ensure compliance with the established software development methodology and software development life cycle (SDLC).
- Ensure direct reports are trained and comply with bank policy, laws and regulations applicable to their roles. Monitor their adherence to internal controls and act to address employee performance issues.
- Understand and adhere to all bank policies, laws and regulations applicable to their role. Complete compliance training; follow internal processes and controls as required.
- Report all compliance issues, violations of law or regulations in accordance with the steps defined in bank policies.
- Perform other job-related duties or special projects as assigned.
- Regular and reliable attendance is an essential function of this position.
Qualifications:
- Bachelor’s Degree in Computer Science or related degree preferred.
- 3+ years of application development leadership experience required.
- 5+ years of programming experience in the desired languages: .NET, C#, SQL.
- 3+ years supervisory experience preferred.
- Valid Nebraska Driver’s License.
Preferred Talents:
- Analytical and detailed
- Effective communicator
- Organized
- Independent with good judgment
- Can multitask and prioritize projects
Working Environment:
Mostly indoor work with occasional exposure to outdoor elements or hazards.
Medium workload – lifting and/or carrying up to 20 pounds frequently and exerting up to 75 pounds of force occasionally.
Some travel required.
This role is eligible for hybrid work from home opportunity under the work from home guidelines upon completion of onboarding.
PLEASE VIEW THE APPLICANT TERMS & CONDITIONS (https://www.ubt.com/ubt-employment-terms-conditions) BEFORE PROCEEDING WITH YOUR APPLICATION
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Union Bank & Trust Co., 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Union Bank & Trust Co. AI Hiring
Union Bank & Trust Co. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lincoln, NE, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.