At a Glance
- Tasks: Design and deliver innovative AI solutions that tackle real business challenges in finance.
- Company: Join a leading global investment house driving an ambitious AI transformation.
- Benefits: Competitive day rate, hybrid work model, and opportunities for professional growth.
- Other info: Dynamic role with excellent career advancement potential in a collaborative setting.
- Why this job: Make a tangible impact with cutting-edge AI technology in a fast-paced environment.
- Qualifications: Proven experience in AI and LLM solutions, strong Python skills, and business engagement.
The predicted salary is between 60000 - 80000 € per year.
We are seeking an experienced AI Engineer to join a leading global investment house embarking on an ambitious AI transformation programme. This is a high-impact contract role focused on building end-to-end agentic AI and LLM-based solutions that solve real business problems across trading, operations, research, and front-office functions. You'll work directly with business stakeholders to understand workflows, design intelligent automation solutions, and rapidly prototype working AI systems that deliver measurable value.
Key Responsibilities:
- Build end-to-end agentic AI and LLM-based solutions from concept to deployment
- Design AI architectures that map to real business problems in investment banking
- Rapidly prototype and iterate AI solutions based on stakeholder feedback
- Move quickly from business brief to working solution - velocity is critical
- Own delivery independently with minimal supervision
Business Engagement & Requirements:
- Engage directly with business stakeholders (traders, analysts, operations, research teams) to understand workflows and pain points
- Translate business requirements into AI solution designs
- Demonstrate AI capabilities and educate stakeholders on art-of-the-possible
- Gather feedback and iterate solutions based on real user needs
- Communicate technical concepts to non-technical business audiences
Technical Implementation:
- Develop robust Python-based AI applications and agent systems
- Integrate LLM capabilities (OpenAI, Anthropic, Azure OpenAI) into business workflows
- Build agentic AI systems that can reason, plan, and execute multi-step tasks
- Implement RAG (Retrieval-Augmented Generation) pipelines for domain-specific knowledge
- Work with vector databases and enterprise data sources
- Integrate AI solutions with existing .NET/C# enterprise systems where required
Innovation & Best Practices:
- Stay current with rapidly evolving LLM and agentic AI landscape
- Recommend appropriate AI frameworks and tools for different use cases
- Establish best practices for responsible AI deployment in regulated environment
- Balance innovation speed with security and compliance requirements
Essential Skills & Experience:
- Proven experience building end-to-end agentic AI or LLM-based solutions in production environments
- Deep understanding of LLM capabilities and limitations - knows when AI is (and isn't) the right solution
- Experience designing AI solutions that map to real business problems, not just technical demos or proof-of-concepts
- Track record of delivering working AI solutions that create business value
Technical Skills:
- Strong Python development skills - production-quality code, not just notebooks
- Ability to architect and build complete AI applications end-to-end
- Experience integrating AI capabilities into existing enterprise systems
- Understanding of software engineering best practices for AI systems
Desirable Skills & Experience:
- Experience with specific LLM providers (OpenAI, Anthropic, Azure OpenAI)
- Familiarity with agent frameworks such as LangChain, LlamaIndex, AutoGen, or similar
- Experience building multi-agent systems and orchestration workflows
- Knowledge of prompt engineering and optimization techniques
Technical Depth:
- C# / .NET background for enterprise integration in financial services
- Experience with RAG pipelines and vector databases (Pinecone, Weaviate, ChromaDB, etc.)
- Understanding of embedding models and semantic search
- Knowledge of fine-tuning and model customization approaches
AI Engineer in London employer: McCabe & Barton
McCabe & Barton is an exceptional employer, offering AI Engineers the opportunity to work on cutting-edge projects within a leading global investment house. With a hybrid work model based in London, employees benefit from a dynamic work culture that fosters innovation and collaboration, alongside competitive day rates and opportunities for professional growth in the rapidly evolving field of AI. The company prioritises employee engagement and values contributions that drive real business impact, making it an ideal environment for those seeking meaningful and rewarding careers.
StudySmarter Expert Advice🤫
We think this is how you could land AI Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. The more people you know, the better your chances of landing that AI Engineer gig.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI projects, especially those involving LLMs or agentic AI solutions. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and understanding of business applications. Be ready to discuss how your AI solutions can solve real-world problems in investment banking.
✨Tip Number 4
Don't forget to apply through our website! We have loads of opportunities that might be perfect for you. Plus, it’s a great way to get noticed by our hiring team directly.
We think you need these skills to ace AI Engineer in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the AI Engineer role. Highlight your experience with agentic AI and LLM solutions, and show how your skills align with the job description. We want to see how you can solve real business problems!
Showcase Your Technical Skills:Don’t hold back on your Python prowess! Include specific examples of projects where you've built end-to-end AI applications. We love seeing production-quality code, so make sure to mention any frameworks or tools you've used that are relevant to the role.
Engage with Business Context:Demonstrate your understanding of how AI fits into the investment banking landscape. Talk about your experience engaging with stakeholders and translating their needs into effective AI solutions. We’re looking for someone who can bridge the gap between tech and business!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensure it gets the attention it deserves. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at McCabe & Barton
✨Know Your AI Stuff
Make sure you brush up on your knowledge of agentic AI and LLM solutions. Be ready to discuss specific projects you've worked on, especially those that involved Python development and integrating AI into business workflows. This will show that you not only understand the theory but also have practical experience.
✨Engage with Stakeholders
Since this role involves direct engagement with business stakeholders, practice how you would communicate complex AI concepts to non-technical audiences. Think about examples where you've successfully translated business needs into technical solutions, as this will demonstrate your ability to bridge the gap between tech and business.
✨Showcase Your Prototyping Skills
Be prepared to talk about your approach to rapid prototyping. Highlight any experiences where you moved quickly from a business brief to a working solution, and how you iterated based on feedback. This is crucial for the role, so having concrete examples will set you apart.
✨Stay Current with Trends
The AI landscape is always changing, so make sure you're up-to-date with the latest LLM developments and best practices. Be ready to discuss recent advancements or tools you've explored, and how they could apply to the investment banking sector. This shows your commitment to innovation and responsible AI deployment.
