Machine Learning Engineer – Fraud Detection

About the role

As a Machine Learning Engineer you will design and operate the systems that enable real‑time fraud detection. You will work across data pipelines, model development, and backend services to ensure that ML models run reliably, efficiently, and at scale.

Key responsibilities

• Build and optimise data pipelines and backend services to process device and behavioural data in real time.
• Develop, deploy and monitor ML models for fraud detection, ensuring production reliability.
• Transform raw data into production‑ready features that feed the fraud detection system.
• Collaborate with platform and backend engineers to integrate models seamlessly.
• Maintain high standards of security, privacy, and regulatory compliance.
• Champion best practices in testing, documentation, and observability.

Required profile

• 5+ years of software engineering experience with strong backend expertise (Go or Python).
• Hands‑on experience applying ML to large datasets using frameworks such as PyTorch or Scikit‑learn.
• Strong SQL skills and familiarity with both relational and non‑relational databases.
• Proven experience delivering end‑to‑end ML systems, including feature pipelines, model deployment, monitoring, and iteration.
• Excellent written and verbal communication in English.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

Required skills

• Go
• Python
• PyTorch
• Scikit‑learn
• SQL
• Relational databases
• Non‑relational databases
• Docker
• Kubernetes
• CI/CD pipelines