Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Founding AI Engineer

Bishopsgate
7 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Senior Data Scientist

Senior Data Scientist (Applied Machine Learning)

Lead Data Scientist

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.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.