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- Senior AI Engineer
Senior AI Engineer
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Tech Stack
About the Role
About Airwallex
Airwallex is the only unified payments and financial platform for global businesses, empowering over 200,000 businesses worldwide including Brex, Rippling, Navan, Qantas, SHEIN. Valued at US$8 billion and backed by T. Rowe Price, Visa, Mastercard, Sequoia and more.
About the team
As the senior engineer in our new Generative AI team, you will own the end-to-end development of AI-driven solutions that redefine how we provide support and automation for both internal teams and external customers. You will shape the underlying architecture and APIs for advanced systems building cutting-edge AI solutions that enable Airwallex to scale. Your contributions will directly influence innovation, set best practices, and establish the foundation for our expanding AI portfolio.
What you'll do
If you are passionate about building AI solutions that directly impact customers, thrive on solving tough technical challenges, and embrace a product-focused mindset, we invite you to shape the future of Finance and AI with us.
Responsibilities:
- Build AI solutions to enable growth and drive internal efficiency across the business
- Design and implement robust API and system architecture for new AI applications
- Collaborate closely with a cross-functional team to bring these AI solutions to life
- Embrace the challenges of a fast-paced, high-growth environment with a focus on innovation
Minimum qualifications:
- 5+ years in backend software development, with a focus on AI
- Experience building internal or customer-facing products with LLMs
- Proficiency in Python
- Expertise in building efficient, large-scale software systems
- Skill in writing high-quality, maintainable code
- Experience in design and development of large-scale distributed, high concurrency, high load, high availability systems
- Excellent communication and mentoring abilities
- A relevant degree in Computer Science, Mathematics or related fields
Preferred qualifications:
- Familiarity with MLOps practices and deployment pipelines for AI-driven products
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