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- Tasks: Build and deploy cutting-edge AI systems using Python in a dynamic startup environment.
- Company: Exciting AI startup backed by a major investment firm in London.
- Benefits: Competitive salary up to £200k, flexible work options, and growth opportunities.
- Why this job: Join top-tier AI engineers and make a real impact in the finance sector.
- Qualifications: Strong Python skills and experience in MLOps or software engineering required.
- Other info: Collaborative culture with a focus on innovation and business value.
The predicted salary is between 72000 - 120000 £ per year.
Overview
New AI startup within major investment firm in London. Up to £200k total compensation.
Are you an experienced AI Engineer with a strong software engineering foundation and a track record of building AI systems that run in production? Come and join an AI startup spun out of a major investment firm, where you will work alongside a team with years of real commercial AI experience behind them. This role is front office facing and we are looking for exceptional technical talent who care about driving business value and enjoy working closely with users.
You can expect to work on greenfield AI projects using Python as a first-class language, with a strong focus on fundamentals, performance, and correctness. The work spans production LLM and ML systems, data pipelines, model deployment, monitoring, and iteration. You will be building and owning systems end to end, including infrastructure and MLOps, across Azure and AWS environments.
In terms of background, we are open to Data Scientist or Software Engineer backgrounds but strong Python is mandatory. We are looking for engineers who really understand Python, including internals and core language behaviour, and who are comfortable designing, deploying, and maintaining AI systems in production. You will be tested on Python fundamentals early in the process. Experience with MLOps, model lifecycle management, and operating AI systems at scale is essential. Financial services experience is a plus, as you will be working closely with investors, but a genuine interest in finance is just as important.
For top-tier AI Engineers, this is a rare opportunity to work alongside some of the most advanced AI practitioners in London, building systems that are used, trusted, and commercially impactful.
Responsibilities:
- Work on greenfield AI projects with a focus on Python development, performance, and correctness.
- Design, deploy, monitor, and iterate production AI systems including LLMs and ML pipelines.
- Own systems end to end, including infrastructure and MLOps across Azure and AWS.
- Collaborate closely with users and front office teams to drive business value.
Qualifications:
- Strong Python expertise, including understanding Python internals and core language behaviour.
- Background in Data Science or Software Engineering; either is acceptable with strong Python as mandatory.
- Experience with MLOps, model lifecycle management, and operating AI systems at scale.
- Willingness to work in a front office facing role and interact with investors; interest in finance is important.
Typical compensation details are provided in the advert, with flexibility for the right profile.
AI Engineer employer: DW Search
Contact Detail:
DW Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the AI field, especially those working at startups or investment firms. Use platforms like LinkedIn to connect and engage with them; you never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI projects, especially those involving Python. This will not only demonstrate your expertise but also give you something tangible to discuss during interviews.
✨Tip Number 3
Prepare for technical interviews by brushing up on Python fundamentals. Since they’ll be testing your knowledge early on, make sure you’re comfortable with the internals and core behaviours of the language.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for top-tier talent, and applying directly can sometimes give you an edge. Plus, it shows you’re genuinely interested in joining our team!
We think you need these skills to ace AI Engineer
Some tips for your application 🫡
Show Off Your Python Skills: Make sure to highlight your strong Python expertise in your application. We want to see your understanding of Python internals and core language behaviour, so don’t hold back on showcasing any relevant projects or experiences!
Tailor Your Application: Take a moment to tailor your application to the role. Mention your experience with MLOps and model lifecycle management, and how these skills can drive business value in our greenfield AI projects. We love seeing candidates who connect their background to what we do!
Be Genuine About Your Interest in Finance: While financial services experience is a plus, a genuine interest in finance is just as important. Let us know why you’re excited about working in this space and how it relates to your career goals. It’ll help us see your passion for the role!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, it shows you’re keen to join our team!
How to prepare for a job interview at DW Search
✨Master Your Python Fundamentals
Since strong Python expertise is a must for this role, make sure you brush up on your Python fundamentals before the interview. Be prepared to discuss core language behaviour and even tackle some coding challenges that test your understanding of Python internals.
✨Showcase Your AI Experience
Highlight any previous projects where you've built or deployed AI systems. Be ready to discuss the challenges you faced, how you overcame them, and the impact your work had. This will demonstrate your hands-on experience and ability to drive business value.
✨Understand MLOps and Model Lifecycle Management
Familiarise yourself with MLOps practices and model lifecycle management. Be prepared to explain how you've managed AI systems at scale in the past, as this knowledge is crucial for the role. Discussing specific tools or frameworks you've used can also set you apart.
✨Express Your Interest in Finance
Even if you don't have direct financial services experience, showing a genuine interest in finance can go a long way. Research the company’s investment focus and be ready to discuss how AI can add value in the financial sector. This will show you're not just technically skilled but also aligned with their business goals.
