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- Tasks: Build cutting-edge AI systems for trading and operational workflows in a fast-paced environment.
- Company: Top hedge fund in London with a focus on innovation and technology.
- Benefits: Competitive salary, dynamic work culture, and opportunities for professional growth.
- Other info: Join a greenfield project with full ownership of tech stack and architecture.
- Why this job: Make a real impact by developing systems that enhance decision-making at scale.
- Qualifications: 5+ years in software engineering with strong Python or Java skills.
The predicted salary is between 80000 - 100000 £ per year.
One of the top hedge funds in London is looking for an Engineer to build production-grade agentic systems that operate directly within trading, research, and operational workflows by ingesting real-time data, reasoning over it, and taking action where it matters. The firm is investing heavily in a low-latency, event-driven AI platform systems that sit alongside humans and augment decision-making at scale.
Front office impact with systems used directly by revenue generating teams in a greenfield environment with no legacy AI platform, giving full ownership to define the stack and architecture.
What you’ll be building:
- Multi-agent orchestration layer coordinating specialised agents (research, execution support, ops automation)
- Task routing, memory management (vector + structured), and tool invocation pipelines
- Real-time data ingestion + reasoning systems
- Kafka-based pipelines ingesting market data, internal signals, and operational events
- Agents reacting to high-volume event streams (millions of messages/day) with sub-second latency constraints
- Retrieval-augmented workflows over internal datasets (research, trade logs, compliance data)
- Building agents that can plan, execute, validate & iterate without human intervention
Tech environment:
- Languages: Python (core AI + orchestration), Java (low-latency services)
- Data & streaming: Kafka, Postgres / kdb+
- AI tooling: OpenAI / open-weight models (LLaMA variants), LangChain / custom orchestration layers
- Infra: Docker, Kubernetes, cloud-native + on-prem hybrid
What we’re looking for:
- 5+ years building complex, production-grade systems
- Strong Python and/or Java
- Strong academic background (top universities preferred)
- Ability to operate in ambiguous, high-impact environments
Agentic AI Software Engineer | Elite Hedge fund | London in City of London employer: Stanford Black Limited
Contact Detail:
Stanford Black Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Agentic AI Software Engineer | Elite Hedge fund | London in City of London
✨Tip Number 1
Network like a pro! Get out there and connect with people in the industry. Attend meetups, conferences, or even online webinars. You never know who might have the inside scoop on job openings or can refer you directly to hiring managers.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to AI and real-time data systems. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by practising coding challenges and system design questions. Brush up on Python and Java, and be ready to discuss your past experiences with building complex systems. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Tailor your application to highlight your experience with low-latency systems and multi-agent orchestration – it’ll make a difference!
We think you need these skills to ace Agentic AI Software Engineer | Elite Hedge fund | London in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the job description. Highlight your experience with Python and Java, and any relevant projects that showcase your skills in building production-grade systems. We want to see how you can fit into our team!
Showcase Your Projects: Include specific examples of projects you've worked on that relate to agentic systems or real-time data processing. This will help us understand your hands-on experience and how you approach complex problems.
Be Clear and Concise: When writing your application, keep it clear and to the point. Use bullet points for key achievements and avoid jargon unless it's relevant. We appreciate straightforward communication that gets to the heart of your qualifications.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Stanford Black Limited
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python and Java. Brush up on your knowledge of Kafka, Docker, and Kubernetes, as these are crucial for the role. Being able to discuss how you've used these tools in past projects will show that you're ready to hit the ground running.
✨Demonstrate Problem-Solving Skills
Prepare to showcase your ability to tackle complex problems. Think of examples where you've built production-grade systems or worked with real-time data ingestion. Be ready to explain your thought process and how you approached challenges, especially in high-impact environments.
✨Understand Agentic Systems
Familiarise yourself with agentic AI systems and their applications in trading and operational workflows. Be prepared to discuss how you would design a multi-agent orchestration layer or handle task routing and memory management. Showing a deep understanding of these concepts will set you apart from other candidates.
✨Ask Insightful Questions
Interviews are a two-way street, so come prepared with thoughtful questions about the firm’s approach to AI and how they envision the future of their systems. This not only shows your interest but also helps you gauge if the company aligns with your career goals. Asking about their greenfield environment can also give you insights into the challenges and opportunities ahead.