Senior Software Engineer II
职位概述 SBS 正在寻找一名熟练的 AI 工程师,加入其在英国或欧盟的数据和人工智能/工程组织。该角色将专注于设计,构建和部署基于人工智能的解决方案,以增强 SBS 产品和客户提供的服务。理想候选人将结合强大的软件工程基础知识与应用 AI / ML 专业知识,使得可扩展,生产级 AI 系统在监管金融服务环境中开发可能。地点:英国或欧元混合动力/远程:遥控 (偶尔旅行需要)责任 AI 解决方案开发 ·设计,构造和部署 AI / ML 模型和智能应用程序跨越 SBS 平台 ·开发利用 SBS 的解决方案:·机器学习 (ML) ·自然语言处理 (NLP) ·生成人工智能 (GenAI / LLMs) ·将业务要求转化为可测量影响的技术人工智能解决方案 Model Deployment & MLOps ·从数据摄入到模型部署和监控,实现并管理端到端的 ML 管道 ·与 DevOps 和工程团队合作以确保可扩展,可靠的生产部署 · 维护和优化模型性能,准确性和效率随着时间推移产品集成 · 将人工智能的能力整合到核心银行,支付和数字平台 · 与产品经理,建筑师和开发人员密切合作,嵌入人工智能面向客户的解决方案 ·支持基于 AI 的 API 开发和微服务架构数据工程合作 ·与数据工程师合作,确保数据质量,可用性和管道完整性 ·通过大规模分布式系统进行结构化和非结构化的数据集工作创新和持续改进 ·保持人工智能,GenAI 以及新兴技术的进展最新信息 ·为实验,原型设计和创新举措做出贡献 ·通过 AI 资格软件架构和设计确定提高自动化,效率和决策机会 ·应用架构:强大的清洁/六角架构,依赖注射,SOLID 原则和基于协议的界面。可以设计结构良好的 Python 服务,清楚地分离问题,可测试组件和可维护的代码库。这不是选项现有系统遵循这些模式,新代码必须一致。· API 设计:设计和维护 RESTful API 合约的经验。理解 OpenAP
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Position Overview
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
Hybrid/Remote: Remote (Ocasional travel required)
Responsibilities
AI Solution Development
· Design, build, and deploy AI/ML models and intelligent applications across SBS platforms
- Develop solutions leveraging:
- Machine Learning (ML)
- Natural Language Processing (NLP)
- Generative AI (GenAI / LLMs)
- Translate business requirements into technical AI solutions that deliver measurable impact
- Model Deployment & MLOps
- Implement and manage end-to-end ML pipelines, from data ingestion to model deployment and monitoring
- Collaborate with DevOps 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
- Innovation & Continuous Improvement
- 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
Qualifications
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.
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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.
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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.
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Data modelling: Can design domain models, define data flows between system components, and make pragmatic schema design decisions. Understands how data structures affect downstream consumers and system evolution.
AI & LLM Application Skills
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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.
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Model-agnostic design: This is a sovereign AI system. The candidate must design against standardised 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 LangGraph, CrewAI, AutoGen, custom agent loops, or any framework — the underlying patterns (ReAct, plan-and-execute, reflection) matter more than the tool.
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Prompt engineering depth: Can demonstrate iterative prompt development for complex tasks. Understands few-shot, chain-of-thought, XML/structured prompting, and the behavioural differences between model families and sizes. Critically, understands that prompt strategies must adapt when moving between frontier and smaller OSS models.
Python Engineering
Production Python: Strong async Python with FastAPI or equivalent framework. Comfortable with Pydantic for data validation, SQLAlchemy for data access, and structured logging (structlog). Writes clean, testable, well-documented code with proper type hints. This is a software engineering role that builds AI systems — not a data science role that happens to write Python. Safeguards for hallucinated tools, loops, partial completion, unsafe actions. Understands async trade-offs, batching, streaming, latency accumulation
Engineering Qualities
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Works within existing systems: The candidate joins a substantial existing codebase and architecture. They must be comfortable reading, understanding, and extending code they did not write — not lobbying to rewrite it.
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Production mindset: Thinks about error handling, retry logic, graceful degradation, observability, and security as first-class concerns. Understands that agentic autonomy creates new failure modes that must be anticipated.
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Clear communicator: Can explain technical trade-offs to non-technical stakeholders. Can write clear design documents. Can articulate why an agentic approach is or is not appropriate for a given problem.
Company Overview
At SBS, we’re more than just a technology company, we’re a global fintech partner helping banks and financial institutions transform, innovate, and grow. With over 5,000 employees worldwide and clients in more than 80 countries, our solutions power everything from digital banking and lending to payments and core banking systems.
As part of 74Software, we’re backed by a group of leading software companies delivering mission-critical solutions worldwide. Our focus is on delivering long-term value, leveraging cutting-edge technology, and fostering strong client partnerships. Join us and be part of a collaborative, forward-thinking team shaping the future of finance.
Why SBS?
At SBS, we’re committed to supporting our employees in every aspect of their lives, from health and wellbeing to financial security and lifestyle perks.
We offer benefits that allow you to thrive at work and beyond, including health coverage, retirement plans, paid time off, flexible work, career development, competitive pay, and global culture perks.
Equal Opportunity Employer
We are committed to providing equal opportunities and ensuring a fair and inclusive recruitment process. We do not discriminate on the basis of age, disability, gender, sexual orientation, race, religion, or any other protected characteristic.
Originally posted on Himalayas