 Finance Digital Transformation - Senior Software Engineer

Job description

Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and curiosity to your job and there's no telling what you could accomplish. Do you love thinking analytically? Just as our customers find value in Apple products, the Finance group finds value for both Apple and its shareholders.

As a senior machine learning engineer in Finance, you’ll play an integral and global role in building the platform, data foundations, and services used for transforming Finance’s organization.
You’ll learn intra-team and business process to build infrastructure and services enabling effective Generative AI and Machine Learning practices. You will help lead the charge by developing strong AI/ML processes and extending platforms in a dynamic environment where you will deal with unique challenges specific to Finance organizations, such as SOX and regulatory compliance. Your ability to instill and proliferate strong software engineering practices into team data science and machine learning processes will be critical.
<h3>Minimum Qualifications</h3>7+ years of experience shipping production web applications, with a proven track record of end-to-end technical ownership.
Proficient in Python, with solid backend engineering experience using FastAPI or Django.
Solid experience developing user interfaces with React or similar modern JavaScript/TypeScript frameworks.
Deep expertise with relational databases (PostgreSQL, MySQL) and Enterprise Data Warehouses (e.g., Snowflake), including data modeling, integration, and complex query optimization.
Deep knowledge of the modern software development lifecycle, CI/CD pipelines, and various types of automated testing.
Strong Kubernetes and cloud platform experience (AWS, GCP, or Azure) in production environments
<h3>Preferred Qualifications</h3> Strong collaboration skills, system thinking, and a proven ability to clearly communicate complex concepts to both technical and non-technical audiences.
Experience building with agentic AI frameworks (e.g., LangChain, LangGraph, Google ADK).
LLMOps or AIOps familiarity, including building AI evaluation pipelines, prompt versioning, and drift detection.
Background in ML algorithms and a track record of their practical application in enterprise settings.
Background in corporate finance, accounting, or supply chain; understanding of SOX, P&L, and close processes.