Artificial Intelligence Engineer

SBS is seeking a skilled AI Engineer to join its Data & AI / Engineering organization in the UK or EU. This role will focus on designing, building, and deploying AI-driven solutions that enhance SBS products and client offerings.

The ideal candidate will combine strong software engineering fundamentals with applied AI/ML expertise, enabling the development of scalable, production-grade AI systems in a regulated financial services environment.

Location: UK or EU

AI Solution Development

• Design, build, and deploy AI/ML models and intelligent applications across SBS platforms
• Natural Language Processing (NLP)
• Generative AI (GenAI / LLMs)
• Translate business requirements into technical AI solutions that deliver measurable impact
• Implement and manage end-to-end ML pipelines, from data ingestion to model deployment and monitoring
• Collaborate with Dev Ops and engineering teams to ensure scalable, reliable production deployment
• Maintain and optimize model performance, accuracy, and efficiency over time

Product Integration

• Integrate AI capabilities into core banking, payments, and digital platforms
• Work closely with Product Managers, Architects, and Developers to embed AI into customer-facing solutions
• Support API development and microservices architecture for AI-driven features

Data Engineering Collaboration

• Partner with data engineers to ensure data quality, availability, and pipeline integrity
• Work with structured and unstructured datasets across large-scale, distributed systems
• Stay current with advancements in AI, GenAI, and emerging technologies
• Contribute to experimentation, prototyping, and innovation initiatives
• Identify opportunities to improve automation, efficiency, and decision-making through AI

Software Architecture & Design

• Application architecture: Strong grasp of clean/hexagonal architecture, dependency injection, SOLID principles, and protocol-based interfaces. Can design well-structured Python services with clear separation of concerns, testable components, and maintainable codebases. This is not optional — the existing system follows these patterns, and new code must be consistent.
• API design: Experience designing and maintaining RESTful API contracts. Understands OpenAPI specifications, versioning strategies, schema evolution, and backward compatibility. Can own the API surface that internal consumers depend on.
• System design: Distributed systems thinking — understands event-driven patterns, asynchronous communication, eventual consistency, and the trade-offs of different integration approaches. Can reason about how components interact across service boundaries.
• Data modeling: Can design domain models, define data flows between system components, and make pragmatic schema design decisions. Understands how data structures affect downstream consumers and the system's evolution.

AI & LLM Application Skills

• LLM application development: Hands‑on experience building production applications powered by LLMs. Must go beyond API calls — RAG pipelines, chain‑of‑thought reasoning, structured output parsing, multi‑step workflows. Candidates who have only fine‑tuned models or only called a single provider’s API do not qualify.
• Model‑agnostic design: This is a sovereign AI system. The candidate must design against standardized API interfaces (OpenAI‑compatible, LiteLLM, or equivalent abstraction layers), not couple to any single provider. Must understand model routing, fallback strategies, and how to build systems that can swap underlying models without application‑layer rewrites. Experience limited to a single commercial API (OpenAI, Google, Anthropic) without abstraction is insufficient.
• Agentic system design: Demonstrated experience designing multi‑step AI systems where the model makes decisions about what to do next. This could be via Lang Graph, CrewAI, Auto Gen, custom agent loops, or any framework — the underlying patterns (ReAct, plan‑and‑execute, reflection) matter more than the tool.
• Prompt engineering depth: Can demonstrate iterative prompt development for complex tasks. Understands few‑shot, chain‑of‑thought, XML/structured prompting, and the behavioral differences between model…