Lead Software Engineer

La Fosse
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Software Engineer - Agentic AI/Machine Learning

Lead Software Engineer - MLOps Platform

Lead Software Engineer (Machine Learning)

Senior Machine Learning Engineer

Lead AI Engineer — Production ML & MLOps Leader

Lead MLOps Engineer

Lead Engineer | Remote | React | Node | AWS | Typescript | £110k + Bonus



Want to make an application Make sure your CV is up to date, then read the following job specs carefully before applying.

La Fosse have partnered with a world-renowned entertainment company that are looking for a Lead Engineer with a strong JavaScript background.


As a Lead Engineer, you'll oversee a team of 3-5 engineers, collaborating closely with Product, Design, Data Science and other stakeholders.


Your mission: build high-performance, accessible systems that create exceptional user experiences.


What they’re Looking For:


  • 5+ years of team leadership experience.
  • Proven success in recruitment, management, and mentoring.
  • Lead the design, development, and operation of innovative software solutions.
  • Expertise in modern web application development (React.js, Node.js, AWS).
  • Proficiency in CI/CD, DevOps, and design patterns.
  • Shape the technical roadmap, influencing technology choices and architecture.
  • AWS certifications and a strong grasp of web architecture.


Bonus experience:


  • Experience with Identity Platforms.
  • Experience in data science and AI integration.


Benefits:


  • Dental insurance and healthcare plan.
  • Bonus
  • Car benefit scheme / Cycle to work
  • Staff discounts
  • Comprehensive pension plan.
  • Bonus Scheme


The role is remote within the UK, with the option for hybrid work based on your preference. The team meets once a quarter in person (Central London office).


Process:

They’ve streamlined their process with a quick 3-stage virtual interview.


If you’re interested in the position and feel you fit some of the requirements, apply now!


Lead Engineer | Remote | React | Node | AWS | Typescript | £110k + Bonus

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.