Applied AI Engineer
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At a glance
- Employment type
- Full-time
- Location
- Ireland · Remote within Ireland
- Workplace
- Remote
- Salary
- €90k-130k/year (EUR)
- Category
- AI
Technologies & skills
Primary technologies
Other technical skills
What you'll be doing
Hands-on engineering role building AI agents, reusable infrastructure, and automation to support a property technology business. Focus on integrating AI workflows into a reliable foundation, collaborating with product and operational teams, and shaping architectural patterns. Fully remote across Ireland and European time zones.
- Build reusable components for agent memory, context management, tool calling, and feedback loops
- Design workflows coordinating AI agents, conventional software, and human input
- Develop evaluation frameworks to assess performance before deployment
- Improve agent behavior traceability, debugging, and enhancement
- Investigate failures and handle operational edge cases
- Turn standalone workflows into reusable infrastructure
- Build development workflows for coding agents to explore repositories, implement changes, review code, and run checks
- Help establish architectural patterns and engineering standards
Key requirements
Must-have
- Strong software engineering experience, including independently taking complex systems from idea to production
- Hands-on experience shipping AI or agentic systems used in a real business
- Practical understanding of tool use, structured outputs, context management, and orchestration
- Experience addressing reliability, latency, evaluation, and observability
- Regular use of coding agents with a considered approach to checking their work
- Strong architectural judgement and ability to connect technical decisions to business needs
- Comfort taking ownership when requirements are still developing
- Clear communication and fluency in English
Nice-to-have
- Strong Python-based AI experience, with willingness to apply it in TypeScript
Role signals
- Technical focus
- AI agent infrastructure, orchestration, and workflow automation
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
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Full job description
Overview
A hands-on engineering role building AI agents, reusable infrastructure and automation that support the everyday operations of a growing property technology business.
This role focuses on bringing existing AI workflows together into a reusable foundation that can support new workflows reliably. You will help build the infrastructure, evaluation and orchestration behind it.
The role is fully remote across the UK, Ireland and European time zones.
What You'll Be Doing
You'll work closely with product and operational teams to understand how work happens, identify useful opportunities for automation and turn them into dependable software.
The remit covers both the AI systems used by the business and how the engineering team uses agents to develop software.
You will be:
- Building reusable components for agent memory, context management, tool calling and feedback loops
- Designing workflows that coordinate AI agents, conventional software and human input
- Developing evaluation frameworks to assess performance before deployment
- Making agent behaviour easier to trace, debug and improve
- Investigating failures and handling operational edge cases
- Turning successful standalone workflows into infrastructure that other processes can use
- Building development workflows where coding agents can explore repositories, implement changes, review code and run checks
- Helping establish architectural patterns and engineering standards as the platform grows
A key part of the job is knowing where an LLM adds value, where conventional code is more dependable and where a person should remain involved.
What You'll Need to Succeed
- Strong software engineering experience, including independently taking complex systems from idea to production
- Hands-on experience shipping AI or agentic systems used in a real business
- Practical understanding of tool use, structured outputs, context management and orchestration
- Experience addressing reliability, latency, evaluation and observability
- Regular use of coding agents, with a considered approach to checking their work
- Strong architectural judgement and the ability to connect technical decisions to business needs
- Comfort taking ownership when requirements are still developing
- Clear communication and fluency in English
The environment is TypeScript-first, using Node.js and PostgreSQL. Engineers with strong Python-based AI experience are also welcome, provided they are comfortable applying that experience in TypeScript.
The Environment
You'll be close to the people using the systems you build, with direct exposure to operational problems and feedback.
Success means:
- Making new workflows quicker to introduce
- Understanding how agents perform before they reach production
- Reducing the manual work needed to support growth
This role will suit someone who enjoys shaping the approach, asking questions and staying involved through deployment, debugging and ongoing improvement.
What's In It for You
- Salary: €90,000–€130,000 p/a
- Fully remote, hiring across the UK, Ireland and European time zones
- Build systems that reduce repetitive operational work and improve the experience of people managing, letting and renting properties
- Join as an early core member of the engineering team, with significant input into architecture, tooling and engineering standards
- Work in a product-focused environment with AI workflows already running in production and a clear need to expand their capabilities
- Technology stack includes TypeScript, Node.js, PostgreSQL, LLM orchestration and agentic workflows
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the Applied AI Engineer role is a hands-on position focused on building and orchestrating AI agent infrastructure, reusable components, and automation to support operational workflows in a property technology business. The role emphasizes end-to-end ownership, architectural decision-making, and close collaboration with product and operational teams.
- AI agent infrastructure and orchestration·High
- Reusable component and workflow development·High
- Evaluation and observability·Medium
- Architectural judgement and business alignment·High
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Recent AI Agent Projects
0–8 minSelect 1-2 projects where you built or shipped agentic systems, focusing on your role, challenges, and outcomes.
Study Orchestration and Evaluation Techniques
8–15 minRefresh your knowledge of orchestration patterns, evaluation frameworks, and observability tools for AI workflows.
Brush Up on TypeScript, Node.js, and PostgreSQL
15–20 minReview code samples or documentation to ensure you can discuss your experience or readiness with the core stack.
Prepare Examples of Architectural Decision-Making
20–25 minThink through situations where you balanced LLMs, conventional code, and human input, and how you aligned with business needs.
Draft Role-Specific Questions
25–30 minWrite down 2-3 thoughtful questions about workflows, team collaboration, and technical direction to ask during the interview.
Talking points
, 6 itemsBuilding and Orchestrating AI Agent Workflows
You should be able to discuss how you've designed, implemented, and improved agentic systems, especially those coordinating AI, conventional software, and human input.
Reusable Infrastructure and Component Design
Prepare examples of creating modular, reusable components (e.g., for agent memory, context management, tool calling) that support scalability and maintainability.
Evaluation and Observability of AI Systems
Demonstrate your approach to evaluating AI workflows before deployment, including frameworks for performance assessment, reliability, and debugging.
Architectural Judgement and Business Alignment
Show how you connect technical decisions to business needs, especially in ambiguous or evolving requirements.
Handling Failures and Edge Cases
Be ready to discuss how you investigate, debug, and resolve operational issues in production AI systems.
TypeScript and Python in AI Engineering
Highlight your experience with the listed stack (TypeScript, Node.js, PostgreSQL, Python) and your adaptability in applying AI expertise across languages.
What to research
, 4 itemsReview AI Agent Orchestration Patterns
Study best practices for orchestrating LLMs, agent memory, context management, and tool use in production systems.
Understand Evaluation and Observability Frameworks
Prepare to discuss frameworks and metrics for evaluating AI agent performance, reliability, and debugging.
Brush Up on TypeScript, Node.js, and PostgreSQL
Ensure you can demonstrate hands-on experience or readiness to work with the core stack in AI/automation contexts.
Prepare Examples of End-to-End AI System Delivery
Select concrete examples where you took ownership of complex AI or agentic systems from concept to production.
Questions to ask
, 6 itemsHow are AI agent workflows currently evaluated and monitored before deployment?
Why ask this? Clarifies the maturity of existing evaluation frameworks and your potential impact.
What are the biggest operational challenges the team faces with current AI systems?
Why ask this? Helps you understand real-world pain points and where your skills can add value.
How do you decide when to use LLMs, conventional code, or human input in a workflow?
Why ask this? Reveals the team's architectural philosophy and decision-making process.
What does success look like for this role in the first six months?
Why ask this? Sets clear expectations and helps you align your contributions.
How closely do engineers collaborate with product and operational teams?
Why ask this? Gives insight into cross-functional collaboration and feedback loops.
What opportunities exist to shape engineering standards and architectural patterns as the platform grows?
Why ask this? Assesses your potential influence on technical direction and standards.
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