Software Engineer (ML/AI)

SHIELD is a device-first fraud intelligence platform that helps digital businesses worldwide eliminate fake accounts and stop all fraudulent activity. 

Powered by SHIELD AI, we identify the root of fraud with the global standard for device identification (SHIELD Device ID) and actionable fraud intelligence, empowering businesses to stay ahead of new and unknown fraud threats.

We are trusted by global unicorns like inDrive, Alibaba, Swiggy, Meesho, TrueMoney, and more. With offices in LA, London, Jakarta, Bengaluru, Beijing, and Singapore, we are rapidly achieving our mission - eliminating unfairness to enable trust for the world.

Responsibilities

As a Software Engineer (ML/AI), You will work closely with the team to build and enhance systems that support proactive identification of fraudulent behavior across our clientele's platforms. This is an opportunity to be part of a high-impact team that combines data, AI, and engineering to protect ecosystems.

• Develop and maintain proactive systems particularly those leveraging Machine Learning and AI
• Enhance and maintain existing AI-based and alert-based systems
• Research and prototype new methods for proactive fraud detection
• Explore and integrate new data signals that can improve fraud identification accuracy
• Conduct comprehensive testing to ensure system reliability and performance
• Write clear documentation for developed systems, workflows, and research findings
• Collaborate closely with other engineers and analysts to achieve shared project goals
  
  
  

• Bachelor's Degree in Computer Science or a related field (or equivalent practical experience)
• Proficiency in at least one of the following languages: Python or Go (Golang)
• Experience working with Relational Databases (e.g., MySQL, PostgreSQL)
• Familiarity with version control systems (e.g., Git)

It will be good to have: 

• Experience working with caching systems (e.g., Redis, Memcached)
• Familiarity with Large Language Model (LLM) APIs or AI frameworks

• Prior experience in the fraud detection or risk domain