East West Bank

Job Description Job Description Overview East West Bank is seeking a highly experienced AI Governance to lead the design, implementation, and operationalization of the bank's enterprise AI governance framework. This role is designed for a hands-on governance leader with deep expertise in AI governance, data governance, risk management, and regulatory compliance within highly regulated industries. The ideal candidate combines practical implementation experience with strong understanding of AI/ML technologies, enterprise controls, data risk, model risk, operational governance, and responsible AI practices. The role will partner across business, technology, data, legal, risk, compliance, audit, and operations teams to establish scalable governance processes supporting AI use cases from intake through production monitoring and enterprise adoption. This position is part of East West Bank's Enterprise AI Strategy & Transformation organization, focused on scaling AI initiatives into governed, measurable, compliant, and enterprise-ready capabilities. Responsibilities Lead the design and implementation of the enterprise AI governance framework, operating model, standards, controls, and governance processes. Establish and operationalize governance processes for AI intake, inventory management, prioritization, risk assessment, technology gating, approval workflows, monitoring, issue remediation, and value realization tracking. Drive governance activities across the full AI lifecycle including ideation, proof of concept, pilot, production deployment, monitoring, model review, and retirement. Partner with business, technology, data, legal, risk, compliance, cybersecurity, and audit teams to ensure AI initiatives align with enterprise governance standards and regulatory expectations. Define governance controls for AI-enabled solutions including data quality, lineage, privacy, explainability, human oversight, auditability, retention, access governance, and operational resiliency. Establish AI governance reporting, dashboards, metrics, and executive-level transparency related to AI adoption, business value, risk exposure, control effectiveness, and remediation progress. Lead governance processes related to LLM, Generative AI, RAG, intelligent automation, and advanced analytics solutions. Evaluate AI technologies, platforms, and vendors to ensure alignment with enterprise architecture, security, compliance, operational, and risk standards. Support regulatory, audit, model risk, and enterprise risk management activities related to AI and advanced analytics environments. Translate governance policies into repeatable operational procedures, evidence requirements, controls, checklists, and implementation standards. Promote enterprise AI governance awareness, adoption, and responsible AI practices across business and technology organizations. Perform other duties as assigned. Qualifications 8+ years of experience in AI governance, data governance, model risk, enterprise risk, compliance, data management, or related governance disciplines within financial services, fintech, insurance, or other highly regulated industries. Strong hands-on experience designing and implementing enterprise AI governance frameworks and operational governance processes in production environments. Demonstrated experience operationalizing AI governance across AI intake, inventory management, prioritization, technology assessment/gating, risk assessment, approvals, monitoring, remediation, and value/metrics reporting. Deep understanding of AI risk, model risk, data risk, operational risk, privacy, explainability, bias, human oversight, and responsible AI principles. Strong understanding of regulatory, compliance, audit, and governance expectations related to AI, model governance, data governance, and enterprise controls. Hands-on experience governing AI/ML, LLM, Generative AI, RAG, and advanced analytics solutions within enterprise environments. Strong familiarity with enterprise governance tooling including Microsoft Purview, Collibra, Alation, Informatica, MLflow, model registries, monitoring platforms, and AI observability solutions. Practical understanding of cloud data and AI ecosystems including Azure, Databricks, Snowflake, AWS, GCP, and modern data platforms. Strong understanding of metadata management, data lineage, access governance, data lifecycle management, retention, and enterprise data quality controls. Experience partnering across business, technology, risk, compliance, legal, cybersecurity, audit, and operations teams to operationalize governance at scale. Strong analytical, executive communication, and stakeholder management skills with the ability to translate complex AI and governance risks into actionable business decisions. Bachelor's degree in Information Systems, Computer Science, Data Management, Risk Management, Business Analytics, or related discipline. Highly Preferred Direct experience governing enterprise AI/GenAI programs within banking or highly regulated industries. Strong familiarity with banking regulatory frameworks and governance expectations including SR 11-7, BCBS 239, CCAR, CECL, AML/BSA, privacy, and enterprise risk management standards. Experience governing LLM platforms including OpenAI, Azure OpenAI, Anthropic Claude, Gemini, Bedrock, or open-source models. Hands-on experience with AI governance related to RAG architectures, vector databases, AI monitoring, prompt governance, and model evaluation frameworks. Experience implementing governance controls supporting AI explainability, fairness, auditability, monitoring, and evidence management. Strong understanding of AI/ML lifecycle management, MLOps, model registries, and production monitoring frameworks. Professional certifications such as CDMP, DAMA, Collibra, Microsoft Purview, risk management, or AI governance certifications. Master's degree or advanced degree in Data Management, AI, Risk Management, Computer Science, or related quantitative discipline. Applicants must have legal authorization to work in the United States. We do not offer visa sponsorship at this time.

Job Description Job Description Overview We are seeking a highly skilled and experienced Release Automation Engineer who will play a crucial role in our software development lifecycle. You will ensure the successful and seamless delivery of high-quality software releases catering to our customers' unique needs. As a Release Automation Engineer, you will work closely with development, operations, and quality assurance teams to ensure that software builds are created, tested, and deployed efficiently and reliably across highly secured on-premises and cloud-based environments. Your expertise is critical in automating the release processes, managing version control, and maintaining the integrity of environments to enable continuous integration and delivery. You will also help define best practices and strategies for using our tools to ensure software projects are released using consistent and repeatable methodologies. You will ensure that services are not interrupted, and features can be rolled back when necessary. Your role will be instrumental in ensuring that our release processes meet business requirements and that our customers receive high-quality software releases that meet their needs. Responsibilities Managing source code repositories and ensuring adherence to version control best practices. Automating the build, test, and deployment processes to enable continuous integration and continuous delivery (CI/CD). Designing and implementing release pipelines to ensure smooth, reliable, and secure software rollouts. Collaborating with development, quality assurance, and operations delivery teams to coordinate release schedules and dependencies. Executing and monitoring the release process and resolving any issues that arise during build, test, or deployment phases. Ensuring that release deliverables are compliant with standards and meet customer expectations. Maintaining documentation related to release procedures, configurations, and modifications. Developing and enforcing policies for hotfixes and emergency releases. Tracking and reporting on release metrics to assess performance and identify areas for improvement. Facilitating communication across teams and stakeholders to keep everyone informed about release statuses and timelines. Managing environment configurations, including deployment and rollback procedures for multiple environments. Staying current with the latest industry trends, tools, and technologies to continuously refine and improve the release process. Perform other duties as assigned. Qualifications 5+ years of DevOps, Software, or Build and Release Automation Engineering experience. Experience with Github, Azure DevOps, BitBucket, GitLab, Cloudbees, Jenkins, and/or other leading source code and release management tools. Proficient with release automation processes like CI/CD, feature flags, version control, build management, and configuration management tools; ability to configure and optimize tools to streamline release and improvement efficiency. Proven experience in defining release criteria, managing end-to-end release processes, and enhancing release gaps. Container (Docker, Kubernetes), web application server, and database deployments and configuration. Applicants must have legal authorization to work in the United States. We do not offer visa sponsorship at this time.

Job Description Job Description Overview East West Bank is seeking a highly experienced Enterprise Data Architect to lead the design, modernization, and governance of the bank's enterprise data architecture strategy and AI-ready data ecosystem. This role is designed for a hands-on enterprise data architect with deep expertise in regulated financial services environments, modern cloud data platforms, enterprise data lifecycle management, and scalable AI/data-driven architectures. The ideal candidate combines strong technical depth with practical implementation experience and the ability to drive enterprise alignment across business, technology, data, risk, compliance, and governance stakeholders. The role will lead data architecture strategy and standards supporting enterprise analytics, AI/ML enablement, governance, regulatory reporting, operational intelligence, and modern data product delivery across the bank. Responsibilities Define and lead the enterprise data architecture strategy, target-state roadmap, and modernization initiatives aligned to business priorities, AI enablement, and regulatory requirements. Design and maintain scalable and secured enterprise data architectures enabling analytics, AI/ML, governance, regulatory reporting, and operational intelligence use cases. Establish enterprise standards (approach, tooling, workflow) for data modeling, metadata management, lineage, reference architecture, semantic layers, integration patterns, and data lifecycle management. Lead architecture design for Azure-native and cloud-based enterprise data platforms including Databricks, Azure Data Lake, Synapse, Microsoft Fabric, Azure Data Factory, and Power BI ecosystems. Define reusable integration patterns across traditional ETLs, APIs, streaming platforms, operational systems, cloud platforms, and analytical environments. Partner with data governance, cybersecurity, risk, compliance, and legal teams to ensure architecture controls support data quality, privacy, retention, lineage, auditability, and regulatory compliance. Drive AI-ready architecture initiatives including lakehouse design, semantic modeling, vectorized data architectures, metadata-driven frameworks, and scalable AI data pipelines. Partner with engineering, analytics, and business teams to operationalize scalable data products and enterprise data domains. Evaluate and implement modern data architecture tooling including metadata/catalog platforms, observability solutions, orchestration frameworks, and governance technologies. Lead enterprise data architecture reviews and governance processes to ensure alignment with approved standards, security controls, and target-state architecture principles. Serve as a senior advisor to executive leadership, translating complex architecture concepts into actionable technology and business strategies. Perform other duties as assigned. Qualifications 8+ years of progressive experience in enterprise data architecture, large-scale data platform modernization, data engineering, or solution architecture within financial services, fintech, insurance, or other highly regulated industries. Strong hands-on experience designing and implementing enterprise data architectures in production environments, not solely strategy or governance roles. Deep expertise in enterprise data lifecycle management including data modeling, integration architecture, metadata management, lineage, master/reference data management, retention, and governance. Strong experience with Azure-native and modern cloud data ecosystems including Azure Databricks, ADLS Gen2, Synapse, Microsoft Fabric, Azure Data Factory, Spark, and distributed data platforms. Strong understanding of AI/ML and advanced analytics architecture requirements including AI-ready data pipelines, semantic modeling, feature engineering support, and scalable analytical data environments. Hands-on experience with enterprise data architecture tooling including Erwin, ER/Studio, Collibra, Microsoft Purview, Alation, Visio, Lucidchart, or equivalent platforms. Strong understanding of modern architecture patterns including lakehouse architecture, event-driven systems, APIs, data mesh, data fabric, and distributed processing frameworks. Practical experience implementing enterprise controls supporting data quality, security, privacy, access governance, auditability, and regulatory compliance. Strong understanding of banking regulatory expectations including BCBS 239, SR 11-7, RCSA, CCAR, CECL, AML/BSA, and enterprise risk reporting principles. Demonstrated ability to influence senior stakeholders and drive alignment across engineering, analytics, infrastructure, governance, cybersecurity, and business teams. Excellent communication and executive presentation skills with the ability to simplify complex technical concepts for non-technical stakeholders. Bachelor's degree in Computer Science, Information Systems, Engineering, Data Architecture, or related technical discipline. Highly Preferred Direct experience building enterprise AI/ML or GenAI-ready data platforms within banking or highly regulated industries. Experience designing vectorized data architectures, semantic search platforms, feature stores, or AI-enabled data product ecosystems. Hands on experience working with vector database tools such as Pinecone, Pgvector, Qdrant, etc. Strong expertise in enterprise metadata, cataloging, governance, and lineage frameworks supporting regulatory transparency and audit readiness. Experience leading enterprise-wide cloud data transformation or modernization initiatives at scale. Hands-on experience with Kafka, event streaming platforms, API gateways, microservices, and real-time data processing architectures. Architecture certifications such as Azure Solutions Architect Expert, Databricks Professional Architect, TOGAF, or equivalent. Master's degree in data science, Computer Science, Engineering, or related quantitative discipline. Proven ability to operate as a highly autonomous enterprise architect balancing strategy, governance, and hands-on technical leadership. Applicants must have legal authorization to work in the United States. We do not offer visa sponsorship at this time.

Job Description Job Description Overview The Senior Cyber Security Engineer will lead and execute security initiatives across the application lifecycle, integrating security into DevOps pipelines, managing vulnerability assessments, and coordinating penetration testing efforts. This role also extends into advanced software assurance, including third-party software analysis, binary-level inspection, and application trust validation, ensuring that both internally developed and externally sourced applications meet the bank's security standards prior to execution within the enterprise environment. Responsibilities Application Security & DevSecOps Integration Embed security controls into CI/CD pipelines using GitHub workflows and automation tools. Collaborate with development teams to implement secure coding practices and threat modeling during design and development phases. Manage GitHub Advanced Security configurations, including secret scanning, push protection, and impact analysis. Security Testing & Vulnerability Management Conduct Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST) using approved tools (e.g., CodeQL, Dependabot OWASP ZAP). Perform manual and automated code reviews to identify vulnerabilities and ensure remediation through code fixes or configuration changes. Maintain accurate mapping of applications to GitHub repositories to support vulnerability tracking and reporting. Advanced Software Analysis & Trust Establishment Perform security analysis of third-party software, including both source code review and compiled binary analysis where source is not available. Conduct binary decomposition and reverse engineering techniques, as appropriate, to evaluate software behavior and identify embedded risks. Support the establishment and execution of a software trust and reputation framework, enabling secure decision-making for application onboarding and whitelisting within the enterprise environment. Analyze open-source and GitHub-hosted code, including dependencies and contribution risk. Partner with AppSec leadership to support application security activities and formalize secure software approval processes. API & Web Application Security Conduct API security assessments and integrate monitoring tools to protect application endpoints. Support WAF policy management and application-layer threat monitoring. Threat Intelligence Integration Integrate threat intelligence insights into software risk assessments, including monitoring for newly disclosed vulnerabilities or exposures in previously approved software. Reassess software trust posture when threat conditions change, ensuring continuous validation of approved applications. Penetration Testing & Third-Party Risk Integrate threat intelligence insights into software risk assessments, including monitoring for newly disclosed vulnerabilities or exposures in previously approved software. Reassess software trust posture when threat conditions change, ensuring continuous validation of approved applications. Qualifications Proven experience in application security, DevSecOps, or software security analysis. Strong hands-on expertise in: SAST/DAST tools and secure SDLC practices GitHub and open-source ecosystems GitHub Advanced Security Experience with third-party software risk analysis, software composition analysis (SCA), or reverse engineering / binary analysis Familiarity with software supply chain security and trust validation frameworks Experience integrating threat intelligence into security decision-making Strong understanding of secure SDLC, threat modeling (e.g., STRIDE), and vulnerability management. Experience coordinating penetration tests and working with third-party vendors.