About The Opportunity
The company’s Anti-Fraud Team leverages advanced blockchain data and AI technologies to protect users and platforms from scams, abuse, and other fraudulent activities. The team develops innovative, scalable solutions that safeguard the integrity of digital ecosystems. The AI Engineer will be responsible for designing and implementing intelligent algorithms to analyze on-chain data, enabling the detection and prevention of fraudulent activities such as scams and reward/campaign abuse. The role requires expertise in transformer models, graph databases, and AI agents to strengthen detection capabilities. The AI Engineer will also lead the deployment of scalable AI services into production, ensuring reliable and real-time fraud detection.
What You’ll Be Doing
- Develop and optimize machine learning models, with a focus on transformer-based architectures, tailored for fraud detection in blockchain data.
- Interpret and analyze on-chain data to extract meaningful features for building robust anti-fraud algorithms.
- Design and implement graph-based approaches using graph databases and graph query languages to identify complex fraud patterns.
- Build intelligent agents using LangGraph or similar frameworks to automate detection workflows and decision-making processes.
- Deploy and maintain AI services and models in production environments, ensuring scalability, reliability, and high performance.
- Collaborate with data engineers, product managers, and software engineers to integrate AI solutions into production workflows.
- Research and apply state-of-the-art techniques in fraud detection, blockchain analytics, and AI.
- Monitor model and service performance, troubleshoot issues, and implement enhancements in response to evolving fraud tactics.
What We Look For In You
- Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
- Proven experience working with on-chain data and a strong understanding of blockchain transaction structures.
- Expertise in transformer models (e.g., BERT, GPT) and their practical applications.
- Experience with graph databases (e.g., Neo4j, Amazon Neptune) and graph query languages (e.g., Cypher, Gremlin).
- Hands-on experience building AI agents, preferably using LangGraph or equivalent frameworks.
- Demonstrated success in deploying AI/ML models and services in production environments (e.g., via REST APIs, microservices, or cloud platforms).
- Proficiency in Python and relevant AI/ML libraries (e.g., PyTorch, TensorFlow, Hugging Face).
Nice to Haves
- Familiarity with blockchain ecosystems and smart contract mechanics.
- Knowledge of AI techniques in anomaly detection.
- Experience with scalable data pipelines and cloud infrastructure (e.g., AWS, GCP).
- Strong problem-solving abilities and critical thinking skills in fraud detection scenarios.
- Excellent communication skills and ability to work in a collaborative, cross-functional environment.