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Payments Engineer (Hybrid)
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Payments Engineer (Hybrid)
Senior AI / Machine Learning Engineer
Our client is the recognized operator and standards body for the UK’s interbank retail payment systems. Together with their sophisticated legacy systems, they operate the nation’s critical payments infrastructure, running the Bacs, Faster Payments, and Image Clearing System. By processing billions of transactions annually, equivalent to over £7 trillion, they ensure the safe, efficient movement of money for individuals and organisations across the UK—making their mission ambitious: to power payments, champion innovation, and serve as the smartest way to move money.
Key Responsibilities
- Design, build, train, and deploy production-grade Machine Learning, Deep Learning, or Natural Language Processing (NLP) models.
- Optimise ML models for enterprise-scale latency, throughput, and performance, ensuring seamless integration with data warehouses and automated workflow bots.
- Collaborate with Data Engineers to design high-quality data pipelines, automated feature stores, and secure data preprocessing routines.
- Develop and scale MLOps workflows for model lineage tracking, code versioning, automated validation gates, and continuous deployment pipelines.
- [Expert Level Focus]: Provide technical direction for AI engineering across teams, evaluate emerging AI methodologies, and deliver high-level mentorship to senior team members.
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Requirements & Qualifications
Technical Expertise (5+ Years Experience Required)
- Hands-on experience in building, shipping, and maintaining ML models in live production environments.
- Deep expertise in Agentic Frameworks such as Microsoft Copilot Studio or semantic kernels for autonomous workflows.
- Proven work with Azure AI Foundry (formerly Azure AI Studio) for deploying and fine-tuning generative AI models and prompt flows.
- Strong background in integrating LLMs with:
- External APIs
- Enterprise data sources
- Robotic Process Automation (RPA) tools to enable autonomous task execution
- Elite proficiency in Python, coupled with experience in:
- Core ML libraries (PyTorch, TensorFlow, Scikit-Learn, Hugging Face)
- MLOps tools (MLflow, Kubeflow, AWS SageMaker, Azure ML)
- Strong computer science fundamentals, including data structures, algorithmic complexity, and clean code principles.
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Leadership & Collaboration
- ability to set and guide the technical direction for AI initiatives
- willingness to mentor senior team members in emerging AI practices
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