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About This Role
People Drive Our Success
Are you enthusiastic, highly motivated, and have a strong work ethic? If yes, come join our team! At Cathay Bank – we strive to provide a caring culture that supports your aspirations and success. We believe people are our most valuable asset and we proudly foster growth and development empowering you to achieve your professional goals. We have thrived for 60 years and persevered through many economic cycles due to our team members’ drive and optimism. Together we can make a difference in the financial future of our communities.
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GENERAL SUMMARY
The VP/FVP, Retail Relationship Manager, primary responsibility is business development (90%) and portfolio management (10%) of new and assigned business clients. This includes identifying and acquiring new quality business relationships, business deposit and loan growth, generating fee income and cross selling to assigned business client portfolio. This position is expected to be an outside sales position, meaning that the incumbent is expected to spend the majority (over 80%) of his/her work time away from the Bank’s offices sourcing 50% or more of leads themselves and meeting with clients and potential clients to sell the Bank’s products.
ESSENTIAL FUNCTIONS
- Proactively solicit new business relationships through calling efforts on prospects developed through referrals from existing clients, cultivation of key referral sources and prospect lists. Actively engage in networking opportunities/events to identify prospective new clients. This includes making outside sales calls to prospective clients.
- Develop and execute a business relationship development plan. The plan should include a comprehensive analysis of business opportunities, market and industry, business client requirements, and sales activity.
- Each Retail Relationship Manager has assigned annual deposit, loan, fee income, new account acquisition, and partner referrals goals as established by retail management.
- Work closely with business partners: Treasury Management, Elavon, Business Banking, Commercial, FX, International, Mortgage, and Wealth Management to ensure that business prospects and client’s needs are properly matched with appropriate depository, lending, treasury, and investment solutions.
- Prepare complete loan/financial packages for underwriting, which includes, but not limited to application, tax returns, financial statements, legal entity documents and any other pertinent business and/or property information. Serves as point of contact for client communication, loan processing, and follow up.
- Manage business prospects/clients, sales pipeline, opportunities, sales activities, and business relationship profiles through Sales Management (CRM).
- Reviews client’s accounts and assigned portfolios to identify, evaluate and determine the appropriate course of action for potential cross sell opportunity. Follow a disciplined relationship development process by identifying steps/strategies necessary to effectively maintain and build relationships with clients and prospects. Consult with manager on strategies and adjusting as needed.
- Ensure compliance with Bank, regulatory and credit requirements with emphasis on best-in-class client service while adhering to required timeframes from the client.
- Partner with branch management and staff to help educate and train with business solutions and to identify new business opportunities.
- Answer all product-related inquiries from potential clients, branch/lending personnel and outside sales contacts.
QUALIFICATIONS
- Education: College graduate with major in related fields or equivalent work experience strongly preferred.
- Experience:
- 5 to 7 or more years of sales and business development experience in banking, basic knowledge of treasury management products and services, lending experience and knowledge of lending regulations and credit/underwriting practices, preferred. Experience working as an outside business development professional with proven record of acquiring businesses with revenue from $2mm to $20mm and above is preferred.
- Ability to generate new business through a consultative sales approach.
- Must possess strong negotiation (rates, terms, collateral requirements) experience.
- Must possess business and lending experience in either a retail or commercial banking environment.
- Must have knowledge and understanding of Bank’s credit policy, risk management, and underwriting requirements.
- Must have strong knowledge and understanding of banking products and services and have demonstrated ability to cross-sell such products.
- Skills/Ability: PC proficiency. Excellent verbal and written communication skills. Bilingual (English/Mandarin or Cantonese) a plus but not required. Ability to work well independently to source new business. Must be organized and detail oriented and able to multi-task. Ability to work effectively in a fast paced, high production and team environment. Excellent time management skills and accustomed to working with deadlines. Ability to assume responsibility and accountability for decision-making. Ability to communicate effectively with all levels of Bank personnel. This position requires a minimum of 50% travel.
OTHER DETAILS
$110K – $130K / year
Pay determined based on job-related knowledge, skills, experience, and location.
This position may be eligible for a discretionary bonus.
Cathay Bank offers its full-time employees a competitive benefits package which is a significant part of their total compensation. It is our goal to provide employees with a comprehensive benefits package to fit their needs which includes, coverage for medical insurance, dental insurance, vision insurance, life insurance, long-term disability insurance, and flexible spending accounts (FSAs), health saving account (HSA) with company contributions, voluntary coverages, and 401(k).
Cathay Bank may collect personal information from potential job candidates and applicants. For more information on how we handle personal information and your applicable rights, please review our Privacy Policy.
Cathay Bank is an Equal Opportunity and Affirmative Action Employer. We welcome applications for employment from all qualified candidates, regardless of race, color, ethnicity, ancestry, citizenship, gender, national origin, religion, age, sex (including pregnancy and related medical conditions, childbirth and breastfeeding), reproductive health decision-making, sexual orientation, gender identity and expression, genetic information or characteristics, disability or medical condition, military status or status as a protected veteran, or any other status protected by applicable law.
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Cathay Bank endeavors to make www.CathayBank.com accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact, Mickey Hsu, FVP, Employee Relations Manager, at (626) 582-7370 or mickey.hsu@cathaybank.com. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications.
Salary Context
This $110K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $170K across 1414 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Cathay General Bancorp, 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 $210,000 based on 1,345 positions with disclosed compensation. This role's midpoint ($120K) sits 43% below the category median. Disclosed range: $110K to $130K.
Across all AI roles, the market median is $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. For comparison, the highest-paying categories include Research Scientist ($260,000) and AI Architect ($251,680). By seniority level: Entry: $125,000; Mid: $202,000; Senior: $240,000; Director: $255,600; VP: $225,000.
Cathay General Bancorp AI Hiring
Cathay General Bancorp has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $130K - $130K.
Location Context
AI roles in Los Angeles pay a median of $204,300 across 172 tracked positions. That's 7% below the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. Highest-paying categories: Research Scientist ($260,000 median, 48 roles); AI Architect ($251,680 median, 9 roles); Research Engineer ($250,200 median, 8 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $220,000. Top-quartile roles start at $260,000, and the 90th percentile reaches $311,800. 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. Research Scientist roles lead at $260,000 median, while AI/ML Engineer roles sit at $210,000. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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