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- Tasks: Build and deploy AI systems in production, focusing on performance and correctness.
- Company: Leading investment firm with a focus on innovation and technology.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Ideal for those looking to kickstart their career in a dynamic environment.
- Why this job: Join exciting greenfield projects and make a real impact in the finance sector.
- Qualifications: Strong Python skills, MLOps experience, and a passion for finance.
The predicted salary is between 50000 - 70000 £ per year.
A leading investment firm is seeking an AI Engineer to build AI systems in production. The role demands exceptional Python skills and experience with MLOps. You will engage in greenfield projects, focusing on performance and correctness, while working closely with users.
Candidates with a strong academic track record and some industry experience are welcomed. Experience in financial services is a plus, but genuine interest in finance is also valued.
Front-Office AI Engineer: Python, MLOps, Production employer: DW Search
Contact Detail:
DW Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Front-Office AI Engineer: Python, MLOps, Production
✨Tip Number 1
Network like a pro! Reach out to folks in the finance and AI sectors on LinkedIn. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Python projects and MLOps experience. This is your chance to shine and demonstrate what you can bring to the table.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss how you've tackled challenges in past projects. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining us. It shows initiative and enthusiasm!
We think you need these skills to ace Front-Office AI Engineer: Python, MLOps, Production
Some tips for your application 🫡
Show Off Your Python Skills: Make sure to highlight your Python expertise in your application. We want to see how you've used Python in past projects, especially in relation to AI and MLOps. Don't hold back on showcasing any cool projects you've worked on!
Talk About Your MLOps Experience: If you've got experience with MLOps, let us know! Share specific examples of how you've deployed machine learning models in production. This will help us understand your hands-on experience and how you can contribute to our greenfield projects.
Express Your Interest in Finance: Even if you don't have direct experience in financial services, we value a genuine interest in finance. Use your application to explain why you're excited about the intersection of AI and finance. It’ll show us that you’re not just looking for any job, but this job!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications better and ensures you don’t miss out on any important updates. Plus, it’s super easy to do!
How to prepare for a job interview at DW Search
✨Master Your Python Skills
Make sure you brush up on your Python skills before the interview. Be ready to discuss your experience with Python in detail, including any projects you've worked on. Practising coding challenges can also help you demonstrate your problem-solving abilities.
✨Showcase Your MLOps Knowledge
Since MLOps is a key part of the role, be prepared to talk about your experience with deploying machine learning models. Discuss any tools or frameworks you've used and how you've ensured performance and correctness in production environments.
✨Understand the Financial Context
Even if you don't have extensive experience in financial services, showing a genuine interest in finance can set you apart. Familiarise yourself with basic financial concepts and be ready to discuss how AI can impact the industry.
✨Engage with Users
This role involves working closely with users, so be prepared to discuss how you've collaborated with stakeholders in the past. Highlight any experiences where you gathered user feedback or adapted your work based on user needs.