Interested in this AI/ML Engineer role at East West Bank?
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About This Role
Introduction:
Since 1973, East West Bank has served as a pathway to success. With over 110 locations across the U.S. and Asia, we are the premier financial bridge between the East and West. Our teams of experienced, multi\-cultural professionals help guide businesses and community members on both sides of the Pacific looking to explore new markets and create new opportunities, and our sustained growth and expertise in industries like real estate, entertainment and media, private equity and venture capital, and high\-tech help build sustainable businesses and expand our associates’ potential for career advancement.
Headquartered in California, East West Bank (Nasdaq: EWBC) is a top\-performing commercial bank with a strong foundation, an enterprising spirit and a commitment to absolute integrity. East West Bank gives people the confidence to reach further.
Overview:
East West Bank is seeking an experienced Senior AI Engineering to design, build, and operationalize enterprise\-grade AI and Generative AI solutions across the Bank. This senior technical leader will translate emerging AI capabilities into secure, scalable, production\-ready banking applications that improve operational efficiency, risk management, customer experience, and employee productivity.
The role is expected to be hands\-on at the outset while helping establish foundational AI engineering capabilities, operating standards, and a small, high\-performing AI engineering team.
Responsibilities:
- Design, develop, and deploy enterprise AI and Generative AI applications for prioritized banking use cases (e.g., customer service, fraud detection, document processing, knowledge management, and operational efficiency)
- Architect LLM\-enabled solutions spanning retrieval\-augmented generation, vector search, agentic workflows, MCP, model orchestration, tool/function calling, and human\-in\-the\-loop controls.
- Build production\-grade services and APIs using Python, FastAPI or Flask, Azure OpenAI, Azure ML, Databricks, ADLS, and modern cloud\-native patterns.
- Integrate AI capabilities into enterprise applications, developer workflows, knowledge management platforms, automation, analytics, and decision\-support processes.
- Establish engineering practices for CI/CD, testing, model evaluation, observability, performance optimization, security, and responsible AI controls.
- Establish reusable AI engineering frameworks, reference architectures, code standards, deployment patterns, and governance controls to accelerate enterprise adoption.
- Partner with business, data, cybersecurity, risk, compliance, legal, and vendor teams to ensure solutions meet regulatory, privacy, auditability, and operational risk expectations.
- Prototype rapidly with stakeholders, convert pilots into scalable implementations, and define measurable adoption and impact metrics.
- Evaluate LLM platforms for accuracy, latency, cost, security, explainability, and fit for regulated enterprise use cases.
- Support hiring, mentoring, and day\-to\-day technical leadership of AI engineers and cross\-functional delivery teams.
- Stay current with emerging AI technologies and advise leadership on practical opportunities, risks, and implementation tradeoffs.
- Perform other duties as assigned.
AI Fluency \& Hands\-On LLM Skills* Hands\-on experience with major LLM platforms, including OpenAI ChatGPT/Codex, Anthropic Claude, Google Gemini, Microsoft Copilot/Azure OpenAI, AWS Bedrock, and open\-source models such as Llama or Mistral.
- Practical experience with prompt engineering, RAG, embeddings, vector databases, LLM orchestration frameworks, agentic workflows, evaluation frameworks, and hallucination mitigation.
- Ability to design AI applications that include data protection, source validation, access control, logging, monitoring, traceability, and human review where appropriate.
- Strong understanding of Responsible AI, model governance, prompt\-injection risks, data privacy, and production controls for LLM\-enabled solutions.
Qualifications:
- Bachelor's degree in Computer Science, Engineering, Data Science, AI/ML, or equivalent practical experience; advanced degree preferred.
- 10\+ years of progressive experience in software engineering, AI engineering, platform engineering or related technology leadership roles, including experience delivering production AI solutions
- Proven experience leading AI, data, automation, or emerging technology initiatives from strategy and experimentation through production delivery.
- Strong hands\-on engineering background in Python, API design, microservices, cloud architecture, distributed systems, data pipelines, CI/CD, testing, observability, and secure software delivery.
- Deep experience with the Azure ecosystem, including Azure OpenAI, Azure ML, Databricks, ADLS, Azure AI Search, and related enterprise integration patterns.
- Experience with LLM frameworks and tooling such as LangChain, LlamaIndex, Semantic Kernel, vector databases, model registries, evaluation frameworks, and monitoring/observability tools.
- Strong process and data discipline, including data quality, lineage, metadata, workflow design, controls, operational risk, and measurable business outcomes.
- Experience in financial services, banking, fintech, insurance, or another regulated industry with strong understanding of compliance, auditability, risk management, and governance.
- Ability to lead cross\-functional teams, influence senior stakeholders, mentor engineers, and translate complex AI capabilities into practical business solutions.
- Strong executive communication skills, including the ability to define AI roadmaps, operating models, standards, adoption plans, and success metrics.
Preferred Qualifications* Master's degree in AI, Computer Science, Data Science, Engineering, or a related field.
- Experience establishing AI engineering teams, platforms, reusable delivery patterns, and enterprise AI standards.
- Experience with copilots, enterprise search, intelligent document processing, workflow automation, and AI\-enabled knowledge management.
- Experience driving AI vendor evaluation and selection processes within regulated environments
- Familiarity with model risk management, third\-party/vendor risk, privacy impact assessments, and regulated technology delivery.
- Experience with MLOps/LLMOps, AI monitoring, evaluation pipelines, model/prompt registries, and production incident management.
- Track record of mentoring senior engineers and building high\-performing technical teams.
Applicants must have legal authorization to work in the United States. We do not offer visa sponsorship at this time.
Compensation: The base pay range for this position is USD $150,000\.00/Yr. \- USD $275,000\.00/Yr. Exact offers will be determined based on job\-related knowledge, skills, experience, and location.
Salary Context
This $150K-$275K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At East West 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($212K) sits 19% above the category median. Disclosed range: $150K to $275K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
East West Bank AI Hiring
East West Bank has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Pasadena, CA, US. Compensation range: $275K - $275K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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