AI Engineer (MLE) Machine Learning Engineer

World HR Services is seeking a Machine Learning Engineer to design systems for fraud detection. The role involves building and optimizing data pipelines, deploying ML models, and ensuring the reliability and efficiency of these systems.

Responsibilities:

• Build and optimize data pipelines and backend services to process device and behavioral data in real time
• Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production
• Turn raw data into production-ready features that feed fraud detection systems
• Collaborate with platform and backend engineers to integrate models seamlessly
• Maintain high standards of security, privacy, and compliance
• Champion best practices in testing, documentation, and observability

Requirements:

• 5+ years in software engineering, with strong backend experience (Go or Python)
• Hands-on experience with applied ML using large datasets (PyTorch, Scikit-learn, etc.)
• Strong SQL skills and familiarity with relational and non-relational databases
• Experience with end-to-end ML systems: feature pipelines, model deployment, monitoring, and iteration
• Excellent communication skills in English, both written and verbal
• Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline
• 5–8 years of experience in software engineering with strong backend and machine learning work
• End-to-end ML model ownership: feature pipelines, model deployment, monitoring, and iteration (not just experimentation)
• Fraud domain experience (bot detection, device fingerprinting, VPN/proxy detection, etc.)
• BS or MS in Computer Science, Engineering, or a related field
• Built latency-sensitive ML systems serving real-time predictions at scale
• Familiarity with ML platform tooling: feature pipelines, drift monitoring, model iteration cycles
• Self-directed: navigates ambiguity and delivers with minimal hand-holding after onboarding
• Domain knowledge in fraud, risk, or cybersecurity
• Familiarity with CI/CD, Docker, Kubernetes, and modern DevOps frameworks
• Understanding of modern browser APIs and high-entropy data collection techniques
• Familiarity with leveraging frontier LLMs for automation
• Depth in backend software engineering over data science
• Experience with Go for backend services (or demonstrated ability to pick up new languages quickly)