Engineering Lead / Integration Lead

Castlethorpe
11 months ago
Applications closed

Related Jobs

View all jobs

MLOps Tech Lead

MLOps Tech Lead

Machine Learning Engineering Lead

Data Scientist

Lead Data Engineer - Remote, High Impact

Lead Data Engineer

Engineering Manager / Integration Lead - Financial Services - Lead Tech Transformation!

Xpertise Recruitment is excited to partner with an innovative player in the financial services sector, and we're on the lookout for a talented Engineering Manager / Integration Lead to join their Technology & Transformation leadership team. This is a unique opportunity to spearhead a brand-new tech capability, driving key technology initiatives and aligning them with broader business goals.

As Engineering Manager, you’ll take on a pivotal leadership role, shaping the future of the organisation’s technology roadmap and fostering a culture of continuous improvement. You’ll focus on driving value-led outcomes and positioning technology as a core business enabler.

This is a rare chance to build a brand-new technology function from scratch. You’ll have the autonomy to shape architecture, lead a talented team, and drive tech-led transformation. You’ll be instrumental in turning this business into a truly modern, technology-driven organisation, with tech at the heart of everything they do.

If you’re passionate about cutting-edge technology, leadership, and making an impact, this is the role for you.

What You’ll Bring:

Strong background in modern tech stacks, cloud-native architectures, and SaaS solutions.
Extensive cloud experience in a cloud-first environment.
Proven experience in leadership roles with a deep understanding of software development, data engineering, and architectural principles.
Proficiency in programming languages (Java, Python, or C#) and ability to engage in technical discussions.
Hands-on experience with cloud platforms (AWS, Azure) and DevOps practices (CI/CD pipelines, automation tools).
Excellent communication skills to bridge technical and non-technical stakeholders.
Leadership & Strategy

Define and drive the overall Technology and Engineering strategy, ensuring technology supports business growth.
Collaborate with senior stakeholders to build a robust technology roadmap.
Lead, mentor, and develop a high-performing tech team, fostering growth and continuous improvement.
Take ownership of team recruitment, as this is a new tech capability within the organisation.
Technical Oversight

Oversee all aspects of Data, Infrastructure, Integrations, Test Engineering, and Architecture.
Drive the design and implementation of scalable, secure, and high-performance technology solutions.
Manage SaaS-based core systems, key integrations, and a data platform.
Own internal cloud infrastructure, CI/CD pipelines, and DevOps practices across the tech stack.
Evaluate and implement software, automation, and data tools to enhance business efficiency.
 
For more information on this role or other similar roles please contact Phil Brindley

Xpertise are acting as an employment agency and business

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.

Jobs

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.

Education Jobs

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.

Jobs

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

🍪 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.