Agile Delivery Lead

MBN Solutions
1 year ago
Applications closed

Related Jobs

View all jobs

Head of DevOps and DataOps

Lead Data Scientist

Applied AIML Lead- Python & Data Science Engineering

Lead Data Scientist

Senior Lead Analyst - Data Science_ AI/ML & Gen AI

Data Science Lead

Agile Delivery LeadHybrid/London, 2-3 days per week in office, Central London£400 Per Day2 Month ContractOutside IR35MBN is working exclusively with a leading AI gaming house in search of an Agile Delivery Lead.The successful candidate will have a strong background in software delivery and be eager to apply their expertise in the Big Data and AI space. This role offers the opportunity to work with advanced technology in a rapidly evolving industry, leading the delivery of innovative data products.Reporting Line:Reports to the Principal Agile Delivery Lead.This is a hands-on role within a collaborative delivery function, where you’ll lead daily operations to drive data product roadmaps. Working closely with data and product teams, you’ll help to ensure a seamless Agile delivery process.What You’ll Do:Partner with product managers to oversee the Agile software delivery process for timely, high-quality feature releases.Collaborate with data scientists and engineers to optimize Agile workflows.Identify delivery risks and issues, addressing them proactively.Maintain updated product roadmaps, facilitating prioritization and resource planning.Operate within a DataOps framework to support our data-driven development process.About You:Experienced in software/project/program delivery with an Agile focus; candidates with transferable skills are welcome.Knowledgeable in Agile methodologies, particularly KANBAN and Scrum.Proficient in Microsoft Azure DevOps.Familiar with the full SDLC, including quality assurance and testing.Adaptable and comfortable with ambiguity in a fast-scaling environment.Strong communicator with an analytical mindset and commercial awareness, especially in time prioritization.

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.

Careers

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.

Jobs

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.

Education

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.

🍪 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.