AI Engineer

SHIELD; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

SHIELD is seeking an AI Engineer to join our fraud intelligence platform and help build AI-powered systems that identify fraudulent behavior across client platforms.

You will design RAG pipelines, work with LLMs (OpenAI, Claude, Gemini), and push rapid production improvements while grounding models in SHIELD's data signals and ensuring performance and cost efficiency.

Qualifications

• Bachelor's degree in CS or related field or equivalent experience.
• Strong Python; Go is a plus.
• Hands-on with LLM APIs (OpenAI, Claude, Gemini).
• Experience shipping production software.
• Experience with relational databases (MySQL, PostgreSQL).
• Familiar with Git.

Responsibilities

• Build and maintain AI-powered systems for proactive fraud detection across client platforms.
• Design and implement RAG pipelines grounded in SHIELD's data signals.
• Develop prompts and evaluations to ensure reliable model performance.
• Prototype and ship new AI features rapidly to production.
• Explore data signals and model capabilities to improve accuracy.
• Conduct tests to ensure reliability, performance, and cost-efficiency.
• Write documentation for systems, prompts, workflows, and research findings.
• Collaborate with engineers and analysts to achieve project goals.

Skills

Python

Go/Golang

LLM APIs

RAG pipelines

Prompting

Production software

Relational databases

Git

Education

Bachelor's Degree in CS

Tools

MySQL

PostgreSQL

Git

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.

• 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.