Interested in this AI/ML Engineer role at Banner Bank?
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About This Role
More than 135 years ago, we started with core values that never go out of style: listen, learn and help businesses and individuals reach their goals. These core values shape our culture, and we were recently Great Place to Work Certified because of our outstanding workplace culture and employee experience. As well, our financial strength and stability are key reasons Forbes named us one of the Best 100 Banks in America for the ninth consecutive year.
With more than $16 billion in assets and 135 branch locations throughout Washington, Oregon, Idaho, and California, we understand our role in the economy and take that responsibility seriously. In addition to offering a source of capital to personal banking clients and businesses of all sizes, we place a high importance on employee volunteerism and donate millions of dollars each year to community organizations.
As our Principal AI \& Cloud Security Engineer, you'll play a critical role in shaping how we securely adopt and scale AI\-powered solutions and Azure cloud technologies across the organization. This senior individual contributor role sets the strategic direction for AI and cloud security, helping development, infrastructure, and security teams build innovative solutions that are secure, resilient, and compliant. If you're passionate about emerging technologies, security architecture, and influencing enterprise\-wide standards, we'd love to hear from you.
### In this role you'll
- Define and lead the enterprise security strategy and architecture for AI\-powered systems and Azure cloud environments, including identity, network security, data protection, and secure cloud and hybrid designs.
- Establish and govern security controls, guardrails, and runtime protections for agentic AI workflows, including tools such as Claude, GitHub Copilot, and custom AI agents.
- Lead threat modeling efforts for AI systems and Azure\-hosted workloads, identifying and mitigating risks associated with adversarial inputs, cloud vulnerabilities, autonomous agents, and emerging attack vectors.
- Evaluate AI\-generated and cloud\-integrated code for security, quality, correctness, and performance, and define enterprise standards for secure code review practices.
- Architect and oversee security tooling and monitoring capabilities that evaluate, monitor, and harden AI agents and Azure resources at scale using technologies such as Microsoft Defender for Cloud, Azure Monitor, and Microsoft Sentinel.
- Embed security governance into the software development lifecycle and Azure DevSecOps pipelines through Security Posture Management, Azure Policy, Key Vault, and Infrastructure\-as\-Code standards.
- Research emerging AI and cloud security threats, regulations, and industry trends, translating insights into actionable strategies and enterprise security improvements.
- Mentor and influence engineering teams on secure AI adoption, cloud security best practices, and enterprise security standards.
### What we're looking for
- You have a Bachelor's degree in Computer Science, Cybersecurity, Information Technology, Engineering, or a related field (Required). An equivalent combination of education and experience can be considered in lieu of a degree.
- You have a Master's degree (Preferred).
- You have 8\+ years of experience in security engineering, secure SDLC, threat modeling, and Azure cloud security, including designing and governing security controls for AI/ML, generative AI, and agentic systems in enterprise cloud environments (Required).
- You have experience within financial services or banking environments (Preferred).
- You hold industry\-recognized security certifications such as CISSP, Azure Security Engineer, GIAC, or similar credentials (Preferred).
### What helps you shine
- You communicate complex security concepts clearly and effectively to both technical and non\-technical audiences.
- You excel at analyzing emerging threats and translating risks into practical enterprise security strategies.
- You bring deep expertise in Azure cloud security, including Microsoft Entra ID, network security, data protection, defense\-in\-depth, and zero\-trust architecture.
- You understand how to secure and govern AI/ML platforms, large language models, and agentic AI architectures.
- You have hands\-on experience evaluating AI\-generated outputs and implementing guardrails, validation controls, and runtime protections.
- You possess advanced secure coding and DevSecOps experience across modern programming languages such as Python, Go, Rust, or C/C\+\+.
- You have strong knowledge of Cloud Security Posture Management and Infrastructure\-as\-Code solutions including Bicep, ARM, and Terraform.
- You understand regulatory, risk, and security considerations common within financial services environments.
### Travel
- Up to 20% travel may be required.
### Our Company Values
- Do the right thing
- Mutual respect
- Teamwork
- Honesty and integrity
### What Our Team Says
"I have the opportunity to learn and grow every day in my current role. I love the work life balance, knowing that we work hard, and strive for high performance but we are celebrated."### Compensation \& Benefits
- Targeted starting salary range (based on geography \& experience): $135,000 \- $178,000
- Comprehensive employee benefits, including: medical, dental, vision, LTD, STD and life
- Paid vacation time, sick time and 11 company paid holidays
- 401k (with up to 4% match)
- Tuition reimbursement
Review Banner's employee benefits at: Employee Benefits \\u007C Banner Bank
Please take time to reviewBanner Bank's Consent \& Privacy notice before applying.
Banner Bank is an Equal Opportunity Employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, citizenship, marital status, age, disability or protected veteran status. Banner Bank does not accept unsolicited resumes from agencies and/or search firms for any job postings. Resumes submitted to any Banner Bank employee by a third\-party agency and/or search firm without a valid written and signed search agreement, will become the sole property of Banner Bank. No fee will be paid if a candidate is hired for a position as a result of an unsolicited agency or search firm referral.
Salary Context
This $135K-$178K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Banner 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($156K) sits 27% below the category median. Disclosed range: $135K to $178K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Banner Bank AI Hiring
Banner Bank has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $178K - $178K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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
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