Artificial Intelligence Engineer

Role overview

Responsibilities

• Design resilient edge AI architectures connecting cloud services and payment platforms.
• Build payment-data pipelines with Azure Data Factory and Azure Databricks for model training and real-time inference.
• Develop LLM and agentic solutions for customer assistance, anomaly explanation and documentation generation.
• Apply machine learning to risk management, fraud detection and transaction optimisation.
• Build Jenkins CI/CD workflows; optimise edge inference for low latency and high throughput.
• Evaluate performance through experiments, backtesting and A/B tests; monitor model drift, data quality and operational incidents.
• Collaborate with payments, product, data, cybersecurity and compliance teams; embed security, privacy, explainability and auditability.
• Document technical and operational designs, mentor colleagues and assess emerging AI and MLOps approaches.

What you’ll bring

• Strong hands-on experience designing, developing and operating production-grade AI and ML solutions.
• Extensive experience with Azure Databricks and Azure Data Factory across data engineering, model development and production operations.
• Practical proficiency with Jenkins for CI/CD, automated testing and safe, repeatable releases.
• Deep understanding of feature engineering, model selection, hyperparameter tuning and evaluation metrics for payments risk and fraud.
• Proven experience building generative AI applications integrated with payment platforms.
• Solid payments-domain knowledge, including transaction flows, settlement, chargebacks, fraud patterns and regulatory considerations.
• Clear communication and structured documentation skills; experience collaborating across distributed teams and hybrid environments.

Technical focus

• Production AI and machine learning, LLMs and agentic applications.
• Azure Databricks, Azure Data Factory and Jenkins.
• Cloud and edge infrastructure, CI/CD, MLOps, observability and responsible AI.