Azure Technical Lead

Reading
10 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer – Azure & Databricks | Hybrid London

Hybrid Lead Data Engineer - Azure Data Platform

Lead Data Engineer (Azure)

Hybrid Data Engineering Lead — Databricks

Lead Data Engineer

Hybrid UK Lead Data Engineer - Data Pipelines & Leadership

Senior Developer

Location: Hybrid - 1 day per week in Reading

Salary: Up to £80,000 + Benefits (including equity)

83zero is proud to be partnering with an innovative start-up that is revolutionising the way companies deliver transformation success. With a cutting-edge SaaS platform, they provide clarity, control, and collaboration, empowering organisations to drive significant ROI and create a more sustainable future.

We are seeking a Senior Developer to play a critical role in shaping the technical future of the company. This is a hands-on leadership position, where you will be responsible for building the UK development capability from the ground up, defining the technical roadmap, and leading key conversations with clients and third-party providers.

If you thrive in a fast-paced start-up environment, enjoy solving complex challenges, and want to be part of a leadership team that values innovation and collaboration, this could be the perfect opportunity for you!

Key Responsibilities:

Lead the development and ongoing enhancement of the company's SaaS platform.
Work closely with the founders, external development teams, and compliance partners.
Take ownership of the Azure DevOps environment, ensuring best practices for CI/CD and cloud management.
Hands-on development using React, Next.js, C#, and .NET.
Manage technical conversations with clients, supporting pre-sales as required.
Monitor Azure spend and recommend improvements to optimize performance and costs.
Ensure compliance with industry standards such as ISO27001 and SOC2.
Lead and mentor a growing internal development team.
Drive future advancements in AI and Machine Learning within the platform.

Key Technical Skills:

HTML5 / JavaScript
React & Next.js 13
Tailwind CSS
C# / .NET
Azure / DevOps Management
GitHub
Jira (for issue tracking and collaboration)

What We're Looking For:

3+ years of hands-on development experience in the core tech stack.
A creative problem solver who enjoys working in a fast-moving start-up environment.
Strong leadership skills with the ability to mentor and grow a technical team.
Excellent communication skills, able to engage with clients and stakeholders.
A flexible and adaptable mindset, ready to take on challenges with enthusiasm.
Experience working with compliance standards (ISO27001, SOC2) is a plus.

What's in it for you?

A salary of up to £80,000 plus equity options.
The chance to shape the technical direction of an exciting start-up.
Work with a passionate and dynamic founding team.
Opportunities for professional growth and exposure to cutting-edge technologies.
A collaborative and fun work environment where your ideas are valued.

If you're excited about this opportunity and want to be part of an ambitious and growing company, we'd love to hear from you

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.