Software Engineer
Job description
Original text imported from Reed
As a Software Engineer you will be a key contributor within a cross-functional engineering squad, responsible for building secure, scalable, and high-quality software with an increasing emphasis on AI-driven capabilities and intelligent agent development. You will work closely with Senior Engineers, the Engineering Team Leader, and the Architect to implement modern engineering practices, integrate AI models and agent patterns, and contribute to the evolution of our cloud-native services. You will already have strong software development experience and have a desire to grow deeper into engineering, while maintaining strong software craftsmanship and delivery discipline.
As we move toward an AI-first engineering strategy with multiple autonomous squads, the Software Engineer plays a vital role in delivering reliable, secure, and modern software while developing skills that contribute to our ecosystem. You will help implement AI capabilities, support cloud-native services, and strengthen the squad’s engineering excellence as we scale.
Key Responsibilities
Software Development & Delivery:
- Design, build, test, and maintain high-quality software components and services across backend, frontend, or full-stack environments.
- Utilise AI Accelerated development tools across the SSDLC to accelerate delivery and product quality.
- Write clean, maintainable, secure code following engineering standards and SSDLC best practices.
- Participate actively in backlog refinement, sprint planning, story estimation, and team ceremonies.
AI Integration & Emerging Skills Development:
- Champion AI Enablement initiatives by embedding AI thinking into product design and delivery, enabling teams to leverage AI, and emerging technologies to enhance functionality, automation, and user experience.
- Use vector databases, embeddings, and retrieval pipelines with support from senior engineers.
- Contribute to building robust tests and evaluation checks for AI behaviours and outputs.
- Follow architectural guidance to ensure AI features remain safe, secure, and reliable.
Quality Engineering & Secure Development:
- Create automated tests, including unit, integration, and functional tests.
- Apply secure-by-design principles in all coding activities, participating in threat modelling where appropriate.
- Contribute to code reviews and continuously improve code quality within the squad.
- Maintain documentation for services, features, and reusable components.
Cloud-Native Engineering & DevOps Practices:
- Deploy and maintain services using CI/CD pipelines.
- Instrument code for observability, logging, and performance insights.
- Participate in incident resolution and root-cause analysis for issues within the squad’s domain.
- Follow best practices for cloud development, working across AWS or Azure environments.
Collaboration & Team Contribution:
- Work closely with Senior Engineers and the Engineering Team Leader to confirm technical designs and implementation details.
- Collaborate with Product Owners to understand requirements and propose feasible approaches.
- Communicate progress, blockers, and technical details clearly within the squad.
- Participate in continuous improvement initiatives and share learnings with peers.
Skills & Experience
- Experience as a software engineer within modern cloud-native environments.
- Strong development skills in at least one of the following languages/frameworks: C# .NET, Node, React, Python, React
- Understanding of AI First development and deployment processes.
- Experience building REST APIs, microservices, or modern frontend applications.
- Good grasp of secure coding, testing strategies, and CI/CD pipelines.
- Work collaboratively in an Agile squad with a focus on quality, delivery, self-reflection and improvement.
- Strong problem-solving skills and willingness to learn and adopt emerging AI and agent technologies.
- Hands-on experience with vector databases, embeddings, or prompt engineering.
- Understanding of AI fundamentals and experience using LLM APIs or AI-enhanced features and Agents.
- Experience with cloud services such as AWS, Azure, or serverless platforms.
- Interest in distributed systems, event-driven architectures, or DDD concepts.
- Familiarity with observability tooling and debugging complex systems.
Key skills
AI-extracted from the job advert
Application advice
5 AI-generated recommendations to maximise your chances.
🤖 Highlight your AI integration experience at the top of your CV as the role emphasises AI-driven capabilities and intelligent agent development
🔧 Showcase specific experience with vector databases, embeddings, or retrieval pipelines as these are explicitly mentioned technical requirements
☁️ Emphasise your cloud-native development skills with AWS or Azure as the role requires working across these environments
🔒 Include examples of secure-by-design principles and threat modelling experience as security is a key focus area
📊 Quantify your testing achievements: "Implemented 150+ automated tests, reducing production bugs by 45%"
Suggested CV bullets
3 bullets our AI drafted for this specific advert, mirroring its ATS keywords.
Add these 3 bullets under your most recent experience:
- •Implemented AI-driven recommendation system using vector databases and embeddings, improving user engagement by 32% across 15,000 daily active users
- •Built comprehensive CI/CD pipeline for cloud-native microservices, reducing deployment time from 45 minutes to 8 minutes while maintaining 99.9% uptime
- •Developed automated testing suite covering 95% code coverage including unit, integration, and functional tests, reducing production bugs by 40%
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Letter preview — tailored to Moorepay
Dear Hiring Manager,
Moorepay's focus on AI-driven capabilities and intelligent agent development represents exactly the direction I want my software engineering career to take. Your emphasis on vector databases, embeddings, and secure-by-design principles aligns perfectly with my experience in building scalable, AI-integrated solutions.
My background in cloud-native development and cross-functional collaboration has prepared me to contribute immediately to your engineering squads. I have hands-on experience with CI/CD pipelines, automated testing frameworks, and implementing observability across distributed systems.
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Interview questions
10 questions generated from this advert.
Technical
- ›How would you implement a retrieval pipeline using vector databases for an AI-powered feature?
- ›Explain your approach to secure-by-design principles when developing cloud-native services
- ›Describe how you would set up observability and logging for a microservice in AWS or Azure
- ›Walk me through your process for writing comprehensive automated tests for AI behaviours
- ›How would you design a CI/CD pipeline for deploying AI-integrated software components?
Behavioural
- ›Tell me about a time when you had to learn a new technology quickly to contribute to a project
- ›Describe a situation where you identified and resolved a security vulnerability in your code
- ›Give an example of how you've collaborated with senior engineers to implement a complex technical solution
- ›Tell me about a time when you had to balance delivery speed with code quality
- ›Describe how you've contributed to improving engineering practices within your team
STAR answer examples
Model answers using the Situation-Task-Action-Result framework. Adapt to your own experience.
Tell me about a time when you had to learn a new technology quickly to contribute to a project
Describe a situation where you identified and resolved a security vulnerability in your code