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- Tasks: Lead AI engineering team to develop cutting-edge trading solutions.
- Company: Join a leading proprietary trading firm at the forefront of AI innovation.
- Benefits: Enjoy competitive salary, flexible work locations, and opportunities for professional growth.
- Why this job: Be part of a dynamic team shaping the future of trading with AI technology.
- Qualifications: 5+ years in AI/ML leadership; expertise in Python, C++, or Java required.
- Other info: This is a senior role ideal for innovative thinkers passionate about AI.
The predicted salary is between 72000 - 108000 £ per year.
Our proprietary trading client is seeking a Head of AI Engineering to lead the development and deployment of AI-driven solutions that optimize trading efficiency and unlock new strategic opportunities. This role is ideal for a hands-on leader with deep expertise in AI/ML infrastructure, real-time data processing, and scalable model deployment in high-performance computing environments.
As the Head of AI Engineering, you will drive the AI strategy, architecture, and execution, leading a team of engineers to build state-of-the-art AI infrastructure and applications. You will collaborate closely with technologists, traders, and quantitative researchers to integrate AI into trading systems, ensuring scalable, low-latency, and production-grade deployments.
Key Responsibilities
- Lead and scale a high-performance AI engineering team, setting technical direction and best practices.
- Develop and optimize AI/ML models and infrastructure for trading and risk management.
- Drive end-to-end AI application development, from concept to deployment and continuous monitoring.
- Architect and enhance MLOps pipelines, feature stores, and model training infrastructure.
- Ensure low-latency, high-reliability AI solutions by optimizing GPU/CPU performance.
- Evaluate and integrate cutting-edge AI frameworks and tools, including TensorFlow, PyTorch, TensorRT, and ONNX.
- Collaborate with quantitative researchers and traders to implement AI-driven strategies.
Qualifications
- Five or more years leading AI/ML engineering teams in high-performance computing or trading environments.
- Seven or more years of hands-on AI/ML development, with expertise in Python, C++, or Java.
- Deep experience in MLOps, AIOps, and AI model deployment at scale.
- Proven track record in designing AI/ML architectures for real-time, mission-critical systems.
- Strong expertise in large language models, retrieval-augmented generation techniques, and fine-tuning AI models.
- Familiarity with compute infrastructure required for high-frequency AI/ML applications.
- Advanced degree in computer science, AI, machine learning, or a related field preferred.
- Exceptional leadership, problem-solving, and communication skills.
This is a senior leadership role for an AI engineering expert passionate about driving innovation in proprietary trading. Only suitable applicants will be contacted.
Head of AI Engineering employer: Tardis Tech
Contact Detail:
Tardis Tech Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Head of AI Engineering
✨Tip Number 1
Network with professionals in the AI and trading sectors. Attend industry conferences, webinars, or meetups to connect with potential colleagues and leaders in the field. This can help you gain insights into the latest trends and possibly get referrals.
✨Tip Number 2
Showcase your hands-on experience with AI/ML projects. Prepare to discuss specific examples where you've led teams or developed solutions that improved trading efficiency. Highlighting your practical knowledge will set you apart from other candidates.
✨Tip Number 3
Familiarise yourself with the latest AI frameworks and tools mentioned in the job description, such as TensorFlow and PyTorch. Being able to speak confidently about these technologies during interviews will demonstrate your commitment and expertise in the field.
✨Tip Number 4
Prepare for technical discussions by brushing up on MLOps and AI model deployment strategies. Be ready to explain how you would architect scalable solutions in a high-performance computing environment, as this is crucial for the role.
We think you need these skills to ace Head of AI Engineering
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in AI/ML engineering, particularly in high-performance computing and trading environments. Use specific examples of projects you've led or contributed to that align with the job description.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and proprietary trading. Discuss how your leadership experience and technical skills make you the ideal candidate for this role. Be sure to mention any relevant frameworks or tools you have worked with.
Showcase Relevant Projects: If applicable, include a portfolio or links to projects that demonstrate your expertise in AI/ML model deployment and real-time data processing. This could be GitHub repositories or case studies that showcase your problem-solving abilities.
Highlight Leadership Skills: Since this is a senior leadership role, emphasise your leadership experience in your application. Provide examples of how you've successfully led teams, set technical direction, and implemented best practices in previous roles.
How to prepare for a job interview at Tardis Tech
✨Showcase Your Technical Expertise
Be prepared to discuss your hands-on experience with AI/ML development, particularly in high-performance computing environments. Highlight specific projects where you've successfully deployed AI solutions, and be ready to explain the technical challenges you faced and how you overcame them.
✨Demonstrate Leadership Skills
As a Head of AI Engineering, leadership is key. Share examples of how you've led teams, set technical direction, and established best practices. Discuss your approach to mentoring engineers and fostering a collaborative environment that drives innovation.
✨Understand the Trading Environment
Familiarise yourself with the proprietary trading landscape and the role of AI in optimising trading strategies. Be ready to discuss how AI can enhance trading efficiency and risk management, and provide insights into how you've integrated AI into trading systems in the past.
✨Prepare for Problem-Solving Scenarios
Expect to face technical problem-solving scenarios during the interview. Practice articulating your thought process when tackling complex issues related to AI model deployment, MLOps pipelines, or real-time data processing. This will demonstrate your analytical skills and ability to think on your feet.
