Interested in this AI/ML Engineer role at Woodforest National Bank?
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Teller Retail Banker II-071515
Description
Take the next step toward your new career today!
Become a part of the diverse and inclusive team within our nationally recognized award-winning Bank that is one of the strongest in the nation. Woodforest National Bank is privately owned, and our Employee Stock Ownership Plan is the largest shareholder. We focus on building relationships and discovering opportunities to better serve our communities and understand the financial needs of every customer we serve. At Woodforest we care and prove it by volunteering with local charities and foodbanks to give back to the communities we serve. By joining Woodforest you will become a part of one of the largest employee-owned banks in the country!
Our Retail Bankers are the face of our organization and are critical in caring for our customers each day. As a Retail Banker you will receive training that will allow you to successfully engage and enhance the customer’s experience by offering products and services that best meet their needs. The well-rounded knowledge base that you develop will prepare you for advancement opportunities and a robust career in banking at Woodforest. Key responsibilities include:
- Achieving individual sales by proactively identifying, marketing, and recommending bank products and services beneficial to customers.
- Processing transactions, opening accounts, and performing account maintenance.
- Handling branch servicing duties such as vault balancing, cash ordering, and maintenance of automated teller machines.
- Employing problem solving skills to address customer inquiries and/or concerns in a manner consistent with the Woodforest Experience training.
Qualifications
Minimum Qualifications/Experience:
- 3 years of relevant and transferrable sales and/or customer service experience;
OR an Associate’s degree and 1 year of relevant and transferrable sales and/or customer service experience;
OR a Bachelor’s degree.
- Previous instore banking experience is preferred, but not required.
- Must be positive and engaging.
Formal Education & Certification:
- High School Diploma or equivalent required.
Work Status:
- Full-time.
Supervisory Responsibility:
- No.
Travel:
- Little to no overnight travel expected. Based on the occasional business need, you may be expected to cover nearby branch locations up to 45 miles from your assigned branch location.
Working Conditions:
- Conditions include standing most of the time, may involve walking, moving, bending, stooping or sitting for brief periods, and occasionally lifting and carrying items up to 30 lbs.
Woodforest offers a comprehensive benefits package. Benefits include 11 holidays, sick leave accrued at 4.62 hours per pay period (bi-weekly). All new regular, full-time employees receive vacation and paid leave time according to their hire date:
- New hires (below SVP) hired Jan/Feb/Mar/Apr = 5 days (40 hours) Vacation and 5 days (40 hours) paid leave
- New hires (below SVP) hired May/Jun/Jul/Aug = 5 days (40 hours) paid leave
- New hire (below SVP) hired Sept/Oct/Nov/Dec = Not eligible for Vacation. Paid leave accrual is determined by the employee’s specific hire date.
For a complete list of benefits please see link for more information:
https://www.woodforest.com/media/ypejqzwn/woodforest\_benefits\_overview-04-25.pdf
Disclaimer:
This job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee. Nothing herein restricts management’s right to assign or reassign duties and responsibilities to this job at any time.
Woodforest is an Equal Opportunity Employer, Including Disability and Veteran
Job: Branch Banking
Primary Location: Illinois-Springfield
Schedule: Full-time
Work Locations: IL - Springfield Walmart-0162 1100 Lejune Dr Springfield 62703
Salary Range: $16.48 to $19.81 per hour
Unposting Date: Ongoing
Organization: Illinois
Salary Context
This $33K-$39K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 33,423 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Woodforest 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. This role's midpoint ($36K) sits 76% below the category median. Disclosed range: $33K to $39K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Woodforest National Bank AI Hiring
Woodforest National Bank has 11 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Springfield, IL, US, Aurora, IL, US, Harvard, IL, US. Compensation range: $39K - $66K.
Location Context
Across all AI roles, 7% (2,320 positions) offer remote work, while 30,984 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 33,423 open positions tracked in our dataset. By seniority: 3,283 entry-level, 20,769 mid-level, 6,381 senior, and 2,990 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,320 positions). The remaining 30,984 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 33,423 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (30,275), AI Software Engineer (749), AI Product Manager (741). 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 (3,283) are outnumbered by mid-level (20,769) and senior (6,381) 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 2,990 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,320 positions), with 30,984 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (21,235 postings), Aws (11,126 postings), Rust (9,803 postings), Python (4,999 postings), Azure (3,220 postings), Gcp (2,707 postings), Prompt Engineering (1,817 postings), Openai (1,487 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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