Backend Software Engineer

CATCHES
united kingdom, united kingdom
1 week ago
Applications closed

Related Jobs

View all jobs

Backend Software Engineer

Senior Backend Engineer - Data Engineer

Software Engineer

Staff Engineer (ML-Native / Software Engineering)

Senior Software Engineer - Data (Basé à London)

Backend Engineer, Issuing

Location:Fully remote with the opportunity of working in a co-working space local to you


About:

CATCHES are a SaaS start-up backed by some of the most influential names in luxury fashion globally. We've partnered with the global leaders in cloud computing and AI to integrate advanced 3D rendering, Artificial Intelligence (AI) and Visual Effects (VFX) techniques to create unparalleled shopping experiences for luxury fashion and exclusive events.


Role:

We are seeking a highly skilled Backend Software Engineer to join our team. The ideal candidate will have experience building APIs and backend services, ideally in C#.NET.

In this role, you’ll build robust, scalable, and secure backend systems powering our SaaS platform. You will collaborate closely with the frontend team, data engineers, and other stakeholders to deliver high-quality software solutions that meet our product's needs.

You’ll have input into technical direction and contribute to shaping backend architecture as we scale.


Responsibilities:

  • Design, develop, and maintain APIs and services primarily usingC#.NET.
  • Build scalable, fault-tolerant systems for a cloud-native environment (primarilyGCP).
  • Implement event-driven workflows usingRabbitMQ.
  • Collaborate with product, design, data, and frontend teams to ship end-to-end features.
  • Own your code in production, participate in code reviews, and improve system observability.
  • Champion clean code, security best practices, and scalable architecture.


Requirements:

  • 4+ years experience building backend systems, ideally in C#.NET.
  • Solid grasp ofPostgreSQLor equivalent relational databases.
  • Cloud deployment experience (GCP preferred, but AWS/Azure welcome).
  • Comfort withevent-driven architecturesandmessage queues.
  • Experience shipping production-grade systems with performance, security, and observability in mind.
  • Ability to work independently in a fast-moving, startup environment.
  • Strong communication skills and a collaborative mindset.
  • Experience delivering pragmatic solutions and implementing iterative design approaches.
  • Strong understanding of engineering fundamentals, including design patterns, SOLID principles, and clean code.


Nice to Have:

  • NoSQL Database experience.
  • Experience withKubernetesor other orchestration systems.
  • Exposure tobare metaldeployments or hybrid cloud environments.
  • DevOps practices: Infrastructure as Code, monitoring, and alerting.
  • Some experience with frontend development or WebGL/3D rendering pipelines.


What Working with Catches Looks Like:

  • Workfully remotewith optional coworking access.
  • Be part of asmall, experienced teamthat values shipping, experimentation, and autonomy.
  • Contribute early to product and architecture decisions.
  • Use cutting-edge tech to shape the future of immersive eCommerce.
  • Enjoy startup pace without burnout: async-first, high ownership, minimal meetings.


Tech Stack:

  • Languages: C#.NET (primary), Go, Python.
  • Databases: Postgres, Redis.
  • Messaging: RabbitMQ.
  • Infra: Docker, Kubernetes, GCP (primary), AWS, Azure & bare-metal.
  • CI/CD: GitHub Actions.

Get the latest insights and jobs direct. Sign up for our newsletter.

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 Non‑Technical Professionals: Where Do You Fit In?

The Model Needs More Than Math When ChatGPT went viral and London start‑ups raised seed rounds around “foundation models,” many professionals asked, “Do I need to learn PyTorch to work in machine learning?” The answer is no. According to the Turing Institute’s UK ML Industry Survey 2024, 39 % of advertised ML roles focus on strategy, compliance, product or operations rather than writing code. As models move from proof‑of‑concept to production, demand surges for specialists who translate algorithms into business value, manage risk and drive adoption. This guide reveals the fastest‑growing non‑coding ML roles, the transferable skills you may already have, real transition stories and a 90‑day action plan—no gradient descent necessary.

Quantexa Machine‑Learning Jobs in 2025: Your Complete UK Guide to Joining the Decision‑Intelligence Revolution

Money‑laundering rings, sanctioned entities, synthetic identities—complex risks hide in plain sight inside data. Quantexa, a London‑born scale‑up now valued at US $2.2 bn (Series F, August 2024), solves that problem with contextual decision‑intelligence (DI): graph analytics, entity resolution and machine learning stitched into a single platform. Banks, insurers, telecoms and governments from HSBC to HMRC use Quantexa to spot fraud, combat financial crime and optimise customer engagement. With the launch of Quantexa AI Studio in February 2025—bringing generative AI co‑pilots and large‑scale Graph Neural Networks (GNNs) to the platform—the company is hiring at record pace. The Quantexa careers portal lists 450+ open roles worldwide, over 220 in the UK across data science, software engineering, ML Ops and client delivery. Whether you are a graduate data scientist fluent in Python, a Scala veteran who loves Spark or a solutions architect who can turn messy data into knowledge graphs, this guide explains how to land a Quantexa machine‑learning job in 2025.

Machine Learning vs. Deep Learning vs. MLOps Jobs: Which Path Should You Choose?

Machine Learning (ML) continues to transform how businesses operate, from personalised product recommendations to automated fraud detection. As ML adoption accelerates in nearly every industry—finance, healthcare, retail, automotive, and beyond—the demand for professionals with specialised ML skills is surging. Yet as you browse Machine Learning jobs on www.machinelearningjobs.co.uk, you may encounter multiple sub-disciplines, such as Deep Learning and MLOps. Each of these fields offers unique challenges, requires a distinct skill set, and can lead to a rewarding career path. So how do Machine Learning, Deep Learning, and MLOps differ? And which area best aligns with your talents and aspirations? This comprehensive guide will define each field, highlight overlaps and differences, discuss salary ranges and typical responsibilities, and explore real-world examples. By the end, you’ll have a clearer vision of which career track suits you—whether you prefer building foundational ML models, pushing the boundaries of neural network performance, or orchestrating robust ML pipelines at scale.