At a Glance
- Tasks: Develop AI infrastructure and applications, collaborating with experts to solve complex challenges.
- Company: Join DRW, a leader in innovative trading technology.
- Benefits: Competitive salary, health benefits, and opportunities for professional growth.
- Other info: Dynamic team environment with excellent career advancement potential.
- Why this job: Work on cutting-edge AI projects that drive real business impact.
- Qualifications: Degree in Computer Science or related field; 3+ years in AI/ML.
The predicted salary is between 70000 - 90000 £ per year.
As an AI Engineer at DRW, you will be an integral member of a collaborative multi-asset trading team. You’ll work on high-impact machine learning (ML) and artificial intelligence (AI) initiatives that are central to our business strategy. In this role, you will build critical infrastructure and tooling that leverages AI and ML technology to enhance productivity, efficiency, and discover innovative paths to challenging business and technology problems. Surrounded by cutting-edge technology and experienced trading teams, you will have the opportunity to tackle fascinating new projects while becoming a subject-matter expert in a critical and innovative role.
Responsibilities
- Drive end-to-end development of AI infrastructure and AI driven applications: from initial proof-of-concept to production deployment and ongoing maintenance.
- Collaborate with technologists, traders, quantitative researchers, and data scientists to identify high-impact opportunities for integrating AI and machine learning into technology and business use cases.
- Implement automated systems for continuous training, validation, and monitoring of models, minimizing downtime and ensuring reliability.
- Provide technical leadership in selecting, integrating, and optimizing AI and ML frameworks, libraries, and tools across diverse hardware and software environments.
- Create and maintain feature pipelines, feature stores, and model stores.
- Develop frameworks to enable scalable, reproducible research.
- Proactively troubleshoot performance bottlenecks, conduct root-cause analyses, and implement solutions to optimize GPU or CPU resource usage.
Qualifications
- Bachelor’s or advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
- 3+ years of experience working with machine learning and artificial intelligence technology.
- Strong understanding of core machine learning and artificial intelligence concepts.
- Excellent programming skills in Python.
- Demonstrated experience in building, validating, deploying, monitoring, and updating production ML and AI models.
- Hands‑on experience with MLOps and AIOps infrastructure and tooling.
- Proficient in problem‑solving and analytical reasoning.
- Exceptional communication and collaboration skills.
- Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX.
- Experience with Large Language Models, including RAG and fine‑tuning techniques.
- Familiarity with compute infrastructure necessary to support operating AI and ML technology.
AI Engineer employer: Drweng
At DRW, we pride ourselves on being an exceptional employer that fosters a dynamic and inclusive work culture. Our commitment to employee growth is evident through tailored mentorship programmes and opportunities for professional development, particularly in the fast-paced environment of university talent acquisition. Located in vibrant hubs across EMEA and APAC, we offer a unique chance to engage with top graduates while enjoying a collaborative atmosphere that values innovation and creativity.
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We think this is how you could land AI Engineer
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We think you need these skills to ace AI Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Drweng, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Drweng. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Drweng
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Drweng!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
