Founding AI Engineer

Bishopsgate
11 months ago
Applications closed

Related Jobs

View all jobs

Quantitative Analyst

Senior Data Analyst

Data Analyst SystmOne

Data Analyst SystmOne

Founding AI Engineer
Up to £130k + equity
London (5 days on-site)

Be part of the founding team at an early stage Fintech.
Best suited to someone who enjoys building and shipping.
Opportunity to build an AI native toolkit from scratch. 
I’m looking for a Founding AI Engineer to join a very early stage (Pre Seed) startup in London. This role is best suited to people who thrive working in highly ambiguous environments and are happy to pivot at the drop of a hat.
 
Startup life isn’t for everyone, so you do really need to be someone that gets excited by the idea of wearing many hats and getting stuck in.
 
The good news is that the business has two years of runway based on funding alone, the even better news is they’re already revenue generating!
 
Being part of the founding team means you’ll have the opportunity to build an AI native toolkit from the ground up. If having a tangible impact on the core product and overall success of the business is something excites you, then this role is for you.
 
The preferred option is to find people who have come through the software engineering route into AI, as opposed to the more traditional route of Data Scientist/ML Engineer. By this I mean you’ll need to be comfortable writing and shipping code and working on AI APIs, less so model building, fine tuning LLMs etc.
 
Essential requirements:

Founder type mindset with a strong product lens.
You value speed and scale over perfection.
Highly autonomous.
Experience building AI agents/agentic systems/architecture/RAG pipelines.
Software engineering background.
Experience developing and deploying production application layer products.
Enjoy the buzz of startup life and want to work with high energy people. 
Just to highlight, this role is 100% on-site. You will need to be happy being in the office more often than not.
 
Unfortunately, sponsorship is not available for this role.
 
Reach out to Jamie Forgan for more information

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jobs

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.