At a Glance
- Tasks: Build and maintain data systems for AI initiatives in trading environments.
- Company: Leading tech firm at the forefront of AI and trading technology.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Based in London with a focus on innovative technology solutions.
- Why this job: Join a dynamic team and shape the future of AI in trading.
- Qualifications: Experience in Python and understanding of trading workflows required.
The predicted salary is between 60000 - 80000 £ per year.
To be successful in this role you will be a seasoned engineer who has delivered impactful AI initiatives with demonstrable positive return on investment. You should have previous experience working at the forefront of technological evolution, adapting as paradigms have shifted. Bring a clear vision for applying AI within front-office trading systems, with a strong understanding of the practical challenges and intricacies involved.
Other skills that would be useful in this role include:
- Hands-on development experience with Python.
- Deep understanding of front office trading workflows, low-latency architectures, market data dynamics, and the operational challenges of integrating AI into real-time trading environments.
- Demonstrated track record of evolving with emerging technologies and leading adoption of new paradigms, with the ability to translate cutting-edge AI capabilities into practical, production-ready solutions.
This role is based out of our London office.
Purpose of the role:
To build and maintain the systems that collect, store, process, and analyse data, such as data pipelines, data warehouses, and data lakes to ensure that all data is accurate, accessible, and secure.
Accountabilities:
- Build and maintain data architecture pipelines that enable the transfer and processing of durable, complete, and consistent data.
- Design and implement data warehouses and data lakes that manage the appropriate data volumes and velocity and adhere to the required security measures.
- Develop processing and analysis algorithms fit for the intended data complexity and volumes.
- Collaborate with data scientists to build and deploy machine learning models.
