AI Engineer

Shield 1; Singapore; On-site; SGD 110,000 - 170,000; Full time

Job summary

SHIELD is a device-first fraud intelligence platform helping digital businesses worldwide eliminate fake accounts and stop fraud. We seek an AI Engineer to build and enhance AI-powered systems that identify fraudulent behavior across client platforms, combining data, AI, and engineering to protect ecosystems.

You will work on LLM-based and agentic workflows, design RAG pipelines, prompts, evaluations, and rapid feature iteration, collaborating with engineers and analysts to deliver high-impact

Qualifications

• Bachelor's Degree in Computer Science or a related field is required or equivalent practical experience.
• Strong proficiency in Python; Go/Golang is a plus.
• Hands-on experience with LLM APIs (OpenAI, Anthropic Claude, Google Gemini) including prompting, RAG, or agentic workflows.
• Experience building and shipping production software with a bias for speed.

Responsibilities

• Build and maintain AI-powered systems for proactive fraud detection; including LLM-based and agentic workflows.
• Design and implement RAG pipelines grounded in SHIELD's fraud intelligence and data signals.
• Develop and iterate prompts and evaluations for reliable model performance.
• Rapidly prototype and ship new AI features 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 docs for systems, prompts, workflows and research findings.
• Collaborate with engineers and analysts to achieve shared goals.

Skills

Python

Go / Golang

LLM APIs

Prompting

RAG pipelines

Production software

MySQL / PostgreSQL

Git

Education

Bachelor's Degree in Computer Science or related

Tools

Redis / Memcached

Vector databases

Job description

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.