Lead AI Engineer, Finance Digital Transformation
Build and Ship: Design and deliver production AI applications that Finance teams across Apple depend on daily—from RAG-powered analytics to agentic automation workflows
Set the Technical Bar: Define engineering standards for AI delivery, from LLM evaluation frameworks to production observability, ensuring we build systems that are auditable, scalable, and SOx-compliant
Lead by Example: Write code alongside your team, whether architecting a new service, optimizing a RAG pipeline, or debugging a production issue
Mentor and Elevate: Guide engineers in AI best practices, conduct architecture reviews, and build a culture of engineering excellence
Drive Production Excellence: Establish SLOs, implement drift monitoring, strengthen CI/CD pipelines, and ensure our AI systems meet Finance's rigorous operational standards
Partner and Deliver: Collaborate with Finance stakeholders to identify high-impact opportunities, prototype solutions rapidly, and ship features that deliver measurable value
Minimum Qualifications
7+ years shipping production web applications with demonstrated technical leadership
Graduate degree in CS, Software Engineering, or related field (or equivalent experience)
Deep expertise in Python and modern JavaScript/TypeScript frameworks (React, Vue)
Proven track record deploying generative AI solutions (RAG, prompt engineering, evaluation frameworks) to production
Strong Kubernetes and cloud platform experience (AWS, GCP, or Azure)
Excellence in system design, code review, testing strategies, and GitOps practices
Experience mentoring engineers and driving technical decisions across teams
Ability to communicate complex technical concepts to both engineering and finance audiences
Preferred Qualifications
LLMOps expertise: evaluation pipelines, prompt versioning, drift detection
Experience with agentic frameworks (LangChain, LangGraph, Google ADK)
Understanding of finance fundamentals (SOx compliance, P&L, financial reporting cycles)
Background in ML algorithms and their practical applications in enterprise settings