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Senior AI/ML Specialists (Payments, Hybrid)
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Senior AI / Machine Learning Engineer
Our client is the recognized operator and standards body for the UK’s interbank retail payment systems. As the infrastructure core supporting the UK economy, they run the critical networks that power Bacs, Faster Payments, and the Image Clearing System, enabling individuals and organizations to transfer money safely and securely. Processing billions of transactions worth over £7 trillion every year, their mission is 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.
- Optimize ML models for enterprise-scale latency, throughput, and performance, ensuring flawless integration with data warehouses and automated workflow bots.
- Partner with Data Engineers to engineer high-quality data pipelines, automated feature stores, and secure data preprocessing routines.
- Establish and scale MLOps workflows to handle model lineage tracking, code versioning, automated validation gates, and continuous deployment pipelines.
- [Expert Level Focus] Set the core technical direction for AI engineering across squads, evaluate emerging AI methodologies, and provide high-level mentorship to senior team members.
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Requirements & Qualifications
- 5+ years (Senior) of professional experience developing, shipping, and maintaining machine learning models in live production environments.
- Agentic Frameworks: Hands-on experience building autonomous workflows, custom plugins, and orchestration logic using Microsoft Copilot Studio or semantic kernels.
- Cloud AI Infrastructure: Proven experience deploying and fine-tuning generative AI models, prompt flow, and vector databases within Azure AI Foundry (formerly Azure AI Studio).
- Tool Integration: Strong background in connecting LLMs to external APIs, enterprise data sources, and robotic process automation (RPA) tools to enable autonomous action execution.
- Elite proficiency in Python paired with extensive experience using core ML libraries and frameworks (such as PyTorch, TensorFlow, Scikit-Learn, or Hugging Face).
- Hands-on experience working with MLOps tracking tools and cloud-based AI infrastructure ecosystems (such as MLflow, Kubeflow, AWS SageMaker, or Azure ML).
- Strong computer science fundamentals, including advanced knowledge of data structures, algorithmic complexity, and clean code principles.
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