Senior AI Engineer

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