AI Engineer

Senior AI Engineering

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.

• 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.

• 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 Lang Chain, llama
  
  Index, Semantic Kernel, vector databases, model registries, evaluation frameworks, and monitoring/observability tools.
• Strong process and data discipline, including…