Senior Backend Software Engineer – Shape a New AI Team at Scale
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At a glance
- Employment type
- Full-time
- Workplace
- Hybrid · 1 day in office
- Salary
- €95k/year (EUR)
- Category
- Back-end development
Technologies & skills
Primary technologies
Cloud & infrastructure
Other technical skills
What you'll be doing
Join a new AI engineering team as a Senior Backend Software Engineer to build scalable, production-grade AI-powered software using Python, TypeScript, or JavaScript. Work on integrating LLMs, RAG, and agentic workflows, owning projects end-to-end and shaping technology and engineering culture.
- Design and build scalable backend and full-stack applications incorporating AI capabilities
- Develop production software using Python, TypeScript or JavaScript, integrating APIs, databases, LLMs and enterprise systems
- Build and improve RAG solutions, AI agents and agentic workflows
- Own features across the full SDLC, including architecture, development, deployment, testing and production
Key requirements
Must-have
- Proven experience as a Senior Software Engineer, Backend Engineer or Full-Stack Engineer building production software at scale
- Strong Python, TypeScript or JavaScript skills with solid backend engineering fundamentals
- Commercial experience with AI/ML, LLMs, RAG or AI agents, ideally taking AI solutions from prototype into production
- Experience with APIs, databases, cloud and modern software engineering practices
Role signals
- Technical focus
- backend
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
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Full job description
Senior Backend Software Engineer – Shape a New AI Team at Scale
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Build the next generation of AI-powered software at massive scale
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Join a brand-new, high-growth AI engineering team building intelligent software experiences
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Work on AI solutions reaching millions of people globally
We are looking for a Senior Software Engineer to join an exciting new Agentic AI / AI Experiences team building the next generation of intelligent software experiences at global scale.
This is a rare opportunity to join a newly established engineering team at the forefront of enterprise AI. You’ll be a strong backend/full-stack engineer working primarily with Python, TypeScript or JavaScript, building scalable production software and integrating AI capabilities including LLMs, RAG and agentic workflows.
You’ll work on production-grade systems that move beyond traditional chatbots, enabling AI to understand business processes, interact with enterprise systems, retrieve information, make decisions, and take meaningful actions on behalf of users.
The platform and products you’re helping build have the potential to reach millions of people, giving you the opportunity to work on challenging engineering problems where backend architecture, APIs, cloud, AI and real-world user experiences come together.
You’ll have significant ownership from design through production, working closely with product managers, business stakeholders and engineers to turn ambitious AI concepts into reliable, scalable products.
Why Join This Team
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Join a new, rapidly growing AI team building the future of intelligent software
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Build scalable backend and full-stack applications using Python, TypeScript, cloud, APIs and AI
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Work with LLMs, RAG and agentic AI in real production environments
-
Own projects end-to-end and help shape the team’s technology and engineering culture
What You’ll Be Doing
-
Design and build scalable backend and full-stack applications incorporating AI capabilities
-
Develop production software using Python, TypeScript or JavaScript, integrating APIs, databases, LLMs and enterprise systems
-
Build and improve RAG solutions, AI agents and agentic workflows
-
Own features across the full SDLC, including architecture, development, deployment, testing and production
What We’re Looking For
-
Proven experience as a Senior Software Engineer, Backend Engineer or Full-Stack Engineer building production software at scale
-
Strong Python, TypeScript or JavaScript skills with solid backend engineering fundamentals
-
Commercial experience with AI/ML, LLMs, RAG or AI agents, ideally taking AI solutions from prototype into production
-
Experience with APIs, databases, cloud and modern software engineering practices
Salary & Benefits
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Competitive base salary up to €95,000
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Bonus up to 20%
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Defined contribution pension scheme + more
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Hybrid working model: 1 day a month in the Cork office
If you’re a strong backend/full-stack software engineer looking to build production AI systems and expand your career into LLMs and agentic AI, we’d love to hear from you.
Contact Serena Akbib on +353 1 960 9972 or send your CV to [email protected].
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the role is for a Senior Backend Software Engineer to join a new AI team, focusing on building scalable, production-grade backend and full-stack applications that integrate advanced AI capabilities such as LLMs, RAG, and agentic workflows.
- Hands-on backend engineering with AI integration·High
- Production-grade software delivery·High
- Ownership across SDLC·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Recent Backend Projects
0–7 minSelect 1-2 projects where you built or scaled backend systems, focusing on architecture, scale, and reliability.
Prepare AI Integration Examples
7–14 minIdentify concrete examples where you integrated LLMs, RAG, or AI agents into production, noting challenges and outcomes.
Refresh API, Database, and Cloud Knowledge
14–20 minReview your experience with API design, database management, and cloud deployments relevant to the listed stack.
Practice Explaining End-to-End Feature Ownership
20–25 minBe ready to walk through your process for delivering features across the full SDLC, including collaboration and testing.
Draft Role-Specific Questions
25–30 minPrepare thoughtful questions about the team's AI strategy, technical challenges, and culture to ask during the interview.
Talking points
, 5 itemsBuilding Scalable Production Systems
You should be ready to discuss examples where you designed or contributed to backend systems that handled large-scale traffic or complex business logic.
Integrating AI Capabilities (LLMs, RAG, Agents)
Prepare to explain how you've incorporated AI/ML models, especially LLMs or retrieval-augmented generation, into real-world applications.
End-to-End Feature Ownership
Demonstrate your experience taking features from design through deployment, including architecture, testing, and production support.
API, Database, and Cloud Experience
Be ready to provide examples of building and integrating APIs, working with databases, and deploying to cloud environments.
Modern Software Engineering Practices
Showcase your familiarity with CI/CD, code reviews, testing, and other best practices that ensure reliability and maintainability.
What to research
, 4 itemsPython, TypeScript, and JavaScript Proficiency
Review your experience and recent projects using these languages, focusing on backend and full-stack development.
AI/ML Integration in Production
Prepare examples of integrating LLMs, RAG, or AI agents into real-world systems, including deployment and monitoring.
API, Database, and Cloud Architecture
Refresh your knowledge of designing and integrating APIs, working with databases, and deploying to cloud platforms.
End-to-End Feature Delivery
Be ready to discuss how you have owned features across the full SDLC, from design to production support.
Questions to ask
, 6 itemsWhat are the main technical challenges the new AI team is currently facing?
Why ask this? To understand the immediate priorities and where your skills can have the most impact.
How does the team approach integrating LLMs and agentic workflows into enterprise systems?
Why ask this? To gauge the maturity of AI integration and the types of problems you'll solve.
What is the typical process for taking an AI feature from concept to production?
Why ask this? To clarify expectations around ownership and collaboration.
How is success measured for backend engineers on this team?
Why ask this? To align your contributions with team and company goals.
What opportunities are there to influence technology choices and engineering culture?
Why ask this? To assess your potential for impact and growth within the team.
How does the team stay up to date with advances in AI and backend technologies?
Why ask this? To understand the team's commitment to learning and innovation.
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