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 an AI Engineer, you will work closely with the team to build and enhance AI-powered 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.
- Build and maintain AI-powered systems for proactive fraud detection, including LLM-based and agentic workflows.
- Design and implement RAG pipelines that ground models in SHIELD's fraud intelligence and data signals.
- Develop and iterate on prompts and evaluations to ensure reliable, measurable model performance.
- Rapidly prototype and ship new AI features, moving quickly from idea to production.
- Explore and integrate new data signals and model capabilities to improve fraud identification accuracy.
- Conduct comprehensive testing to ensure system reliability, performance, and cost-efficiency.
- Write clear documentation for systems, prompts, workflows, and research findings.
- Collaborate closely with engineers and analysts to achieve shared project goals.
Requirements
- Bachelor's Degree in Computer Science or a related field (or equivalent practical experience).
- Strong proficiency in Python (Go/Golang is a plus).
- Hands-on experience building with LLM APIs such as OpenAI, Anthropic (Claude), or Google Gemini — including prompting, RAG, or agentic workflows.
- Experience building and shipping production software, with a bias for moving fast.
- Experience working with relational databases (e.g., MySQL, PostgreSQL).
- Familiarity with version control systems (e.g., Git).
It will be good to have:
- Experience with vector databases or embedding-based retrieval.
- Experience designing structured outputs from LLMs (e.g., JSON or schema-constrained generation).
- Familiarity with token optimization and cost/latency tuning.
- Familiarity with evaluation frameworks or methods for LLM outputs.
- Experience working with caching systems (e.g., Redis, Memcached).
- Prior experience in the fraud detection or risk domain.