Interested in this AI/ML Engineer role at Bank OZK?
Apply Now →Skills & Technologies
About This Role
Why Bank OZK?:
Founded on a legacy of more than 120 years in banking, Bank OZK is much more than just a company. We’re nationally recognized as an industry leader in financial services. That means we combine exceptional service with innovative technologies to deliver smart solutions to our clients across the country. We’re investing in small businesses, fueling economies in local communities and changing skylines in the largest cities across America. Here, we're not simply filling roles. We're fostering even greater careers.
The foundation for a great career starts with an exceptional team and a comprehensive benefits package. We believe in providing our dedicated team members with the best resources to support their physical, mental and financial wellbeing, including generous PTO, 401(k) matching, health, dental, vision (and pet!) insurance as well as special perks and discounts. Learn more about Bank OZK benefits.
Job Purpose & Scope:
Leads the development of scalable artificial intelligence (AI) systems, focusing on prompt tuning, retrieval-augmented generation (RAG) pipelines, and microservices architecture to advance Bank OZK’s enterprise AI initiatives. Collaborates across teams to deliver production-ready AI applications that drive innovation, operational efficiency, and enhanced customer experiences.
Essential Job Functions:
- Designs and optimizes prompts for Gen AI and Agentic models/systems, large language models (LLMs) such as GPT, LLaMA, Mistral, and Gemini to achieve business-focused outcomes.
- Develops and maintains retrieval-augmented generation (RAG) pipelines and agentic workflows using tools like LangGraph, AutoGen, Milvus, Pinecone, or Weaviate.
- Builds cloud-native, scalable microservices to support AI applications in production environments.
- Implements AI-powered applications using Python and Java, ensuring robust and maintainable codebases.
- Supports data ingestion, preprocessing, and feature engineering to enable effective model training and deployment.
- Assists in the deployment, monitoring, versioning, and scaling of AI systems on cloud platforms.
- Collaborates with senior engineers, product managers, and research scientists to deliver enterprise-grade AI solutions.
- Monitors advancements in AI/ML research and tools to inform ongoing development and strategic direction.
- Performs other duties as assigned.
Knowledge, Skills & Abilities:
- General knowledge of model fine-tuning and optimization techniques.
- General knowledge of distributed systems and microservices architecture beyond AI applications.
- General knowledge of machine learning fundamentals.
- Strong problem-solving skills and ability to translate business challenges into technical solutions.
- Effective collaboration and communication skills across cross-functional teams.
- Proficient skill with agentic AI orchestration tools such as LangGraph, AutoGen, and Camunda.
- Strong organizational, critical thinking, and time management skills.
- Proficient skill in Python and Java for AI application development.
- Strong skills in LLMs, NLP, and GenAI applications.
- Skill with cloud platforms (e.g. AWS, Azure, GCP) and containerization technologies (e.g. Docker, Kubernetes).
- Skill with databases including SQL, NoSQL, and vector databases.
- Ability to maintain confidentiality and exercise discretion and sound judgment in technical decision-making.
- Ability to work under minimal supervision.
- Ability to manage multiple projects or assignments and demonstrate flexibility in a continually changing environment.
- Ability to build scalable microservices and RAG systems.
Basic Qualifications:
- Bachelor’s degree in Computer Science, AI/ML, Data Science, or a related field, or commensurate work experience required.
- 4+ years of experience in AI/ML engineering or applied AI system development, required.
Job Expectations:
Operate customary equipment and technology used in a business environment, with or without accommodation.
Note: This description is not an exhaustive list of all job functions, duties, skills, and job standards required. Other job functions, duties, skills, and standards may be added. Management reserves the right to add or change the job requirements at any time.
#LI-KC1 #OZK EEO Statement:
Bank OZK is an equal opportunity employer and gives consideration for employment to qualified applicants without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, disability status, protected veteran status, or any other characteristic protected by federal, state, and local law. Member FDIC.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Bank OZK, 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 $212,040 based on 13,780 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 $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Bank OZK AI Hiring
Bank OZK has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in Dallas, TX, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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.