Machine Learning Engineer (Remote)

Fruition IT
London
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Lead Machine Learning Engineer £90-160k + equity London Hybrid Read to dominate a $trillion industry? You'll be working closely with the CTO and founders, building out an agentic AI system that enables clients to fully utilise available AI/ML tooling. By delegating tasks to machines and integrating this with the human team, the system you build will accelerate product and project plans to new hights. Think, humans and AI agents working in perfect harmony. This role is for a builder, a doer, not someone who wants to stay high level or theoretical. You will have a strong influence on the direction of the core product offering, and will be at the forefront of a currently developing technology. Interest is high for this product, and the market is ripe for disruption. Role: Develop AI agents that can execute tasks autonomously Architect and develop systems for the organisation, communication and task delegating for AI agents (and humans!) Design and develop production ready, cloud deployed products Ensure performant monitoring and evaluation of systems and products Enable to seamless integration of multiple AI/ML models across the system Use various data bases, including graph Be a driving force in technical decision making, solve problems autonomously Requirements: Expertise in AI & ML Engineering, significant commercial experience Strong Python programming experience Experience with the latest ML models Commercial experience with LLMs Passionate about RAG, LLMs, or Graph Networking NLP experience Track record building & deploying production ready ML systems Passion for the potential of AI & ML Deploying into and building on AWS PhD Desirable : Agentic AI experience, or orchestration experience that would be a plus Graph DB Knowledge graphs Projects or public speaking outside of day job Logistics : ~ Flexible working ~ London office with space for you to come in/ meet the team ~£90-160k + equity ~ Wealth creation opportunity ~ Build a product with a passionate team with a genuine upshot We are an equal opportunities employer and welcome applications from all suitably qualified persons regardless of their race, sex, disability, religion/belief, sexual orientation, or age.

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.