About Appier

Appier is a software-as-a-service (SaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.

About the role

Appier's mission is to make AI easier for everyone to use. To achieve this, we build AI systems to connect heterogeneous data in the cross-screen era, to process information efficiently while achieving our goals, and to solve new and exciting real-world problems. Are you passionate about AI, and looking to be part of this flourishing AI age? Do you want to witness how different types of data play a key role in concurrent AI systems and contribute to improving the capability of these systems? Do you want to build incredible AI systems and watch them grow to meet our product needs? If yes, we'd love to talk to you.

Responsibilities

Build a flexible framework to speed up the development process of AI models, with a focus on Large Language Models (LLMs) serving.
Turn exciting AI prototypes/ideas into products, leveraging LLMs and other advanced AI technologies.
Develop next-generation AI backend systems related to large-scale real-time data access, collection, analytics and monitoring.
Establish and maintain MLops processes and tools, including model deployment, monitoring, and automation.
Continuously improve the quality of AI production systems, particularly those utilizing LLMs.

About you

[Minimum qualifications]

BS/BA degree in Computer Science or related field.
Know basic software testing (able to write unit test for algorithms)
Experience in Unix/Linux environments.
Strong problem-solving skill and passion for learning new technologies.
Great communication skills to work side-by-side with scientists, and collaborate with engineers, product managers and other teams.
Write clean and maintainable code including code related to LLM serving systems.

[Preferred qualifications]

Familiarity with the machine learning flow of building a data-driven AI system including LLM development and deployment.
Experience in query authoring and optimization of SQL/NoSQL databases.
Experience with distributed computing and machine learning frameworks (Spark, Hadoop or Flink), for scaling LLM serving systems.
Experience in one of the following programming languages: Python/C++/Scala.