Senior Software Engineer – Agentic AI | Shape a New AI Team at Scale
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At a glance
- Employment type
- Full-time
- Location
- Cork, Ireland
- Workplace
- On-site
- Salary
- €95k/year (EUR)
- Category
- Full-stack
Technologies & skills
Primary technologies
Cloud & infrastructure
Other technical skills
What you'll be doing
Join a new AI engineering team as a Senior Software Engineer to build production-grade AI agents and agentic workflows at scale. You'll work on full-stack solutions using Python, TypeScript, or JavaScript, integrating LLMs, APIs, and cloud infrastructure, with significant ownership from design to deployment.
- Design and build production AI agents and agentic workflows
- Develop scalable software using Python, TypeScript or JavaScript
- Integrate LLMs with APIs, databases and enterprise systems
- Contribute to architecture and technical design
- Own features across the full SDLC, from design to deployment
- Build testing, observability and monitoring into AI applications
- Debug and optimise agent reliability, performance and quality
- Contribute to CI/CD, Kubernetes and cloud infrastructure
Key requirements
Must-have
- Proven experience building full-stack software at scale, ideally 1M+ users
- Commercial experience with AI/ML, LLMs or AI agents
- Strong Python, TypeScript or JavaScript skills
Role signals
- Technical focus
- full-stack AI agent development, architecture, integration
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
Senior Software Engineer – Agentic AI | Shape a New AI Team at Scale
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Build the next generation of AI agents at massive scale
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Join a brand-new, high-growth AI engineering team shaping agentic 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 work on production-grade AI agents and agentic workflows that move beyond traditional chatbots enabling AI to understand business processes, interact with enterprise systems, 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 AI, software architecture 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. The team is growing rapidly, making this an excellent time to join and play a key role in shaping both the technology and engineering culture.
Why Join This Team
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Join a new, rapidly growing AI team building the future of agentic experiences
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Build AI solutions with the potential to reach millions of users globally
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Work across LLMs, agentic AI, RAG, cloud, APIs and Kubernetes
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Own projects end-to-end, from architecture to production
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Shape the team’s technology, engineering standards and AI strategy
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Collaborate with talented engineers, product leaders and business stakeholders
What You’ll Be Doing
-
Design and build production AI agents and agentic workflows
-
Develop scalable software using Python, TypeScript or JavaScript
-
Integrate LLMs with APIs, databases and enterprise systems
-
Contribute to architecture and technical design
-
Own features across the full SDLC, from design to deployment
-
Build testing, observability and monitoring into AI applications
-
Debug and optimise agent reliability, performance and quality
-
Contribute to CI/CD, Kubernetes and cloud infrastructure
What We’re Looking For
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Proven experience building full-stack software at scale, ideally 1M+ users
-
Commercial experience with AI/ML, LLMs or AI agents
-
Strong Python, TypeScript or JavaScript skills
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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1 day a month onsite within the Cork office
If you’re looking to expand your career in AI at scale, 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 Software Engineer to join a new AI team focused on building scalable, production-grade agentic AI solutions using technologies like Python, TypeScript, JavaScript, LLMs, and Kubernetes. The position emphasizes end-to-end ownership, architectural contributions, and integrating AI agents with enterprise systems at global scale.
- Full-stack engineering at scale·High
- AI/ML and LLM integration·High
- Production reliability and observability·Medium
- Cloud infrastructure and CI/CD·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review and Summarize Large-Scale Project Experience
0–7 minList and outline your most relevant full-stack projects, focusing on scale, architecture, and your specific contributions.
Refresh AI/ML and LLM Integration Knowledge
7–14 minStudy recent work with LLMs, agentic workflows, and integration patterns with APIs and enterprise systems.
Revisit Kubernetes and CI/CD Practices
14–20 minPrepare to discuss your experience with cloud infrastructure, Kubernetes, and automated deployment pipelines.
Prepare Examples of Testing and Monitoring AI Systems
20–25 minGather concrete stories about implementing observability, testing, and monitoring in production environments.
Draft Role-Specific Questions
25–30 minWrite down thoughtful questions about the team's challenges, technical stack, and growth opportunities.
Talking points
, 5 itemsBuilding Scalable Full-Stack Applications
You should be ready to discuss your experience designing and deploying software that serves large user bases, as the listing highlights scale (1M+ users) as a key requirement.
Developing and Integrating AI Agents
Prepare examples of how you've built or integrated AI/ML solutions, especially those involving LLMs or agentic workflows, since this is central to the team's mission.
End-to-End Feature Ownership
Demonstrate your ability to take features from design through deployment, including collaborating with stakeholders and ensuring production reliability.
Cloud Infrastructure and CI/CD
Showcase your experience with Kubernetes, CI/CD pipelines, and cloud environments, as these are explicitly mentioned as part of the responsibilities.
Testing, Observability, and Monitoring in AI Systems
Be prepared to discuss how you ensure quality, reliability, and performance in AI-driven applications, including your approach to testing and monitoring.
What to research
, 4 itemsReview Large-Scale Full-Stack Projects
Prepare detailed examples of systems you've built or scaled to serve large user bases, focusing on architecture, challenges, and outcomes.
Deepen Knowledge of LLMs and Agentic AI
Refresh your understanding of large language models, agentic workflows, and their integration with APIs and enterprise systems.
Brush Up on Kubernetes and CI/CD
Review your experience with Kubernetes, cloud infrastructure, and CI/CD pipelines, especially as they relate to deploying AI or full-stack applications.
Prepare Examples of Testing and Monitoring AI Systems
Gather examples of how you've implemented observability, testing, and monitoring in production AI or software systems.
Questions to ask
, 6 itemsWhat are the team's current priorities and biggest technical challenges in building agentic AI solutions?
Why ask this? To understand where your skills can have the most immediate impact.
How does the team approach integrating LLMs with existing enterprise systems and APIs?
Why ask this? To clarify the technical landscape and integration complexity.
What is the expected level of ownership and autonomy for engineers on this team?
Why ask this? To gauge how much influence you'll have over architecture and product direction.
How does the team ensure reliability, observability, and monitoring in production AI agents?
Why ask this? To learn about the team's engineering standards and practices.
What opportunities are there to shape the team's technology choices and engineering culture as it grows?
Why ask this? To assess your potential for impact and leadership.
How does the team collaborate with product managers and business stakeholders during the SDLC?
Why ask this? To understand cross-functional workflows and communication expectations.
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