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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, strong Python/Java skills, and a top academic background.
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 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
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working at hedge funds. Use LinkedIn to connect and engage with them; you never know who might have an inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving real-time data processing or AI systems. This will give potential employers a taste of what you can do and how you can contribute to their team.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and Java skills. Practice coding challenges that focus on low-latency systems and event-driven architectures. The more comfortable you are, the better you'll perform when it counts!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got a streamlined process that makes it easy for you to get your application in front of the right people. Plus, it shows you’re serious about joining our team!
We think you need these skills to ace Agentic AI Software Engineer | Elite Hedge fund | London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the job description. Highlight your experience with Python, Java, and any relevant AI systems you've built. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for building agentic systems and how you can contribute to our cutting-edge projects. Let us know why you're excited about this role at our hedge fund.
Showcase Relevant Projects: Include any projects that demonstrate your ability to work with real-time data ingestion and low-latency systems. We love seeing practical examples of your work, so don’t hold back!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get the attention you deserve. Let's make it happen!
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 discuss specific challenges you've faced in building complex systems. Think about how you approached problem-solving in high-pressure situations, especially in ambiguous environments. Use examples that highlight your ability to reason over real-time data and make decisions quickly.
✨Showcase Your Academic Background
Since the firm prefers candidates from top universities, be ready to talk about your academic achievements. Highlight any relevant coursework or projects that align with agentic AI systems. This can help establish your credibility and show that you have a strong foundation in the principles behind the technology.
✨Ask Insightful Questions
Prepare thoughtful questions about the firm's approach to AI and how they envision the future of their systems. This not only shows your genuine interest in the role but also gives you a chance to assess if the company culture and goals align with your own. Asking about their greenfield environment can also give you insights into the level of innovation expected.