Zechen Dai: Multimodal AI Engineer

Zechen Dai

MSc Management of Information Systems & Digital Innovation, 2026

Based in:

Coventry

Came to study at Warwick from:

China

First job:

IELTS speaking tutor in Shanghai

Wish you had known when applying for jobs:

The importance of visa requirements and sponsorship policies. For many international graduates applying for jobs in the UK, visa eligibility can be one of the most important factors in the application process.

Some employers clearly explain whether they offer sponsorship, while others provide limited or unclear information. Understanding this earlier would have helped me assess opportunities more efficiently and focus my applications on roles where the visa arrangements were realistic.

I would advise international students to research an employer’s sponsorship policy before investing significant time in an application. It is also useful to ask clear questions during the recruitment process rather than relying on general or uncertain answers. This does not replace the need to find a suitable role, but it can make the job search more focused and informed.

Best piece of advice received:

Some of the most useful career advice I received came from the WBS CareersPlus team. When I first arrived at Warwick, I did not know how to write a professional CV. They showed me how to improve its structure, present my experience clearly, and focus on evidence rather than general statements.

That experience taught me to view a CV from an employer’s perspective. It should not simply list what you have done; it should clearly show the skills you used, the results you achieved, and why your experience is relevant to the role. With their support, I produced a much stronger CV and developed a skill that I still use today.

Multimodal AI Engineer (RA); WBS Gillmore Centre for Financial Technology

Describe your current role and what attracted you to it.

In my current role, I support academics at the Gillmore Centre for FinTech by turning research ideas into working technical solutions. I use my engineering skills to develop local AI applications based on open-source models, train and adapt AI models, and design research experiments. This often involves working with NVIDIA GPUs in a Linux environment, writing code, testing different approaches, and collecting data for research.

I was attracted to this role because I have always been interested in technology and enjoy understanding how computer systems work. As AI has developed rapidly in recent years, this interest naturally led me to study the field in greater depth. The role allows me to combine my knowledge of AI, programming, and computer systems with practical research problems. It also gives me the opportunity to keep learning while contributing to projects with real research value.

What’s your favourite part of the role?

My favourite part of the role is the process of solving unfamiliar problems. A research idea may initially be quite broad, so I need to break it down, identify the right technical tools, and turn it into a working system or experiment. I particularly enjoy the moment when a difficult problem is finally solved after testing different methods and learning new concepts. Every project introduces me to new models, tools, and research questions. This continuous learning is one of the most rewarding parts of the job.

What are the key skills you learnt at Warwick that have helped you with your career to date?

Before coming to Warwick, I studied Accounting and Auditing. Although the degree gave me useful analytical skills, I realised that I was more interested in technology and wanted to move towards a technical career.

My Warwick degree was particularly valuable because it welcomed students from both technical and non-technical backgrounds. I developed skills in programming, data analysis, information systems, digital innovation, and the theory behind artificial intelligence. Before the programme, I had very limited coding experience, but the course gave me the foundation and confidence to build technical projects independently.

Warwick also taught me how to approach complex problems in a structured way, learn unfamiliar tools quickly, and connect technical solutions with wider business and research needs. These skills have been essential in my current role.

Did you have a specific career path in mind when you chose to study at Warwick?

I knew that I wanted to move away from accounting and towards a career related to technology. My ideal direction was an engineering or AI-related position where I could build systems. At the same time, I was also considering roles such as data analysis, technology consulting, or AI consulting. As I gained more experience in programming, AI, and technical projects at Warwick, my goals became much clearer. I discovered that I most enjoyed practical engineering work, particularly turning ideas into working applications and solving technical problems.

What top tips do you have for Warwick graduates who would like to work in your sector?

Try to gain practical experience as early as possible. Technical and AI-related internships are valuable, but research assistant positions, university projects, and short-term technical roles can be equally useful. Warwick students can also explore opportunities through platforms such as Unitemps and university research centres.

It is also important to build projects outside the classroom. Reading about AI is useful, but employers are more interested in what you can actually create, test, and explain. A small but complete project can demonstrate programming ability, problem-solving skills, and genuine interest in the field.

Finally, make use of the wide range of learning resources now available. AI tools, open-source projects, online courses, documentation, and research papers have made technical knowledge much more accessible. Regular self-directed learning is essential because the sector develops very quickly.

What does a typical day look like for you?

I usually start my day at around 8am and prepare breakfast before beginning work at 9am. At the start of the working day, I review any new tasks or research ideas from the academics I support.

I normally begin by analysing the problem and identifying the components, models, data, and computing resources that may be required. I then plan the structure of the program or experiment and consider how the idea can be turned into a practical solution.

In the afternoon, I usually prepare technical notes or requirement documents, write code, configure models, and run tests. Depending on the project, I may also analyse results, troubleshoot errors, or adjust the system based on feedback.

After work, I have dinner and relax by reading AI and technology news, watching films, or playing video games. I am both a film enthusiast and a video game fan.

What has been your greatest career challenge to date and how did your experience and skills help overcome it?

One of my greatest challenges has been working with AI models that do not initially meet the requirements of a research project. Using an existing model is often only the starting point. Its performance, configuration, memory use, or context capacity may need to be adapted for a specific experiment.

For example, a project may require a model to process a much longer context than it supports by default. Solving this type of problem requires more than basic programming. It involves understanding the model architecture, reviewing its original documentation, studying related research, and testing different configuration or context-extension methods.

I approach these challenges by breaking the problem into smaller parts and testing each assumption carefully. My experience with Linux, GPUs, programming, and AI models helps me identify possible causes, while my research skills help me locate and understand the relevant technical material. The process can take time, but it has taught me to be patient, systematic, and comfortable with uncertainty.

What ambitions do you have for the future?

My ambition is to build a long-term career across AI, data, engineering, and computer science. I hope to use my technical knowledge to help companies improve their products, solve important problems, and create greater commercial value.

In the longer term, I would like to help organisations adapt to continuous technological change. New technologies can quickly reshape entire industries, so companies need to keep learning and improving to remain competitive. I hope to contribute both technical skills and practical ideas that help an organisation grow, respond to change, and avoid being left behind.

What should current students or recent alumni be doing to move their careers forward?

Students and recent graduates should apply actively and avoid relying on only a small number of applications. The graduate job market is competitive in many countries, so it may be necessary to submit a large number of well-targeted applications. Applying widely increases the range of opportunities available, but each application should still be adapted to the role and employer.

It is also valuable to complete different internships before entering full-time employment. Practical experience helps students understand what different roles involve and which type of work suits their interests and strengths.

International students should also communicate actively with professors, classmates, alumni, and career advisers. These conversations can provide useful information about industries, career paths, vacancies, and visa-related issues. Building these relationships early can make the transition from university to employment much easier.