Senior Software Engineer | Agentic AI | €650 Daily

Archer · Recruitment agency
Contract•Full-stack•€650/day (EUR)•Dublin, Ireland · Remote within Ireland · Hybrid
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At a glance

Employment type
Contract
Workplace
Hybrid
Day rate
€650/day (EUR)
Category
Full-stack

Technologies & skills

Primary technologies

Python.NETPHPC#

Other technical skills

LangChain

What you'll be doing

Join a global team as a Senior Software Engineer to build intelligent, scalable AI applications using C#/.NET or Python. You'll design AI agents, RAG-based solutions, and work with LangGraph and LangChain, shaping modern AI platforms for enterprise use. Hybrid role in Dublin, 12-month contract.

  • Build intelligent, scalable applications using C#/.NET or Python
  • Design and develop AI agents and agentic workflows
  • Build RAG-based solutions for enterprise data
  • Use LangGraph and LangChain to orchestrate agent behaviour and workflows
  • Develop and integrate MCP-based tools and services
  • Experiment, iterate, and refine solutions based on real-world requirements

Key requirements

Must-have

  • Strong commercial software engineering experience with C#/.NET or Python
  • Hands-on experience building AI agents or Agentic AI solutions
  • Practical experience with LangGraph, LangChain, and RAG
  • Strong software engineering fundamentals across APIs, integrations, and scalable applications
  • Interest in building with AI and experimenting with new technology

Role signals

Technical focus
AI agents, agentic workflows, scalable applications
Architecture / system design
Indicated in the listing
Hands-on vs management
Hands-on

Full job description

Senior Software Engineer | Agentic AI | €650 Daily

  • Turn AI ideas into production

  • Engineer the next AI platform

  • €650 daily | 12-month contract

AI is moving beyond experimentation — and this programme is focused on making it work in the real world.

A global organisation is building a new generation of AI capability, bringing together modern software engineering, intelligent agents and enterprise technology. As a Senior Software Engineer, you’ll be part of the team turning ambitious ideas into usable, scalable products.

This is a role for an engineer who enjoys more than simply writing code. You’ll be involved in shaping solutions, experimenting with new approaches, solving difficult technical problems, and figuring out how Agentic AI can genuinely work within an enterprise environment.

You’ll work across AI agents, RAG, LangGraph, LangChain, and MCP, while still applying the engineering fundamentals that make software reliable, scalable, and production-ready. There is plenty of room to experiment — but the end goal is always to build something that works.

Why This Role Stands Out

  • From idea to reality — see your work evolve from an initial concept into a working solution.

  • Modern AI engineering — work with some of the technologies defining the next generation of software.

  • Engineering meets AI — combine your existing software expertise with deep exposure to Agentic AI.

  • Solve problems, don’t just tickets — work on complex challenges where there isn’t always an obvious answer.

What You’ll Be Doing

  • Build intelligent, scalable applications using C# / .NET or Python.

  • Design and develop AI agents and agentic workflows.

  • Build RAG-based solutions capable of working with enterprise data.

  • Use LangGraph and LangChain to orchestrate agent behaviour and workflows.

  • Develop and integrate MCP-based tools and services.

  • Experiment, iterate and refine solutions based on real-world requirements.

What You’ll Bring

  • Strong commercial software engineering experience with C# / .NET or Python.

  • Proven hands-on experience building AI agents / Agentic AI solutions.

  • Practical experience with LangGraph, LangChain and RAG.

  • Strong software engineering fundamentals across APIs, integrations and scalable applications.

  • A genuine interest in building with AI, experimenting with new technology and solving problems from first principles.

For more information, contact [email protected]

For additional opportunities, visit the Archer Recruitment website.

Interview prep pack

Grounded in this listing. Use it to prepare examples before you apply.

Your interview focus

Based on this listing, the Senior Software Engineer role focuses on building production-ready AI agent solutions using C#/.NET or Python, with a strong emphasis on LangChain, LangGraph, and RAG in an enterprise context. The position requires hands-on engineering, experimentation, and the ability to turn innovative AI concepts into scalable, reliable products.

  • AI agent development·Medium
  • LangChain/LangGraph proficiency·High
  • Scalable software engineering·High
  • Experimentation and iteration·High

Only have 30 minutes?

Follow a focused preparation plan based on this job.

Start 30-minute prep

Your 30-minute plan

  1. Review LangChain, LangGraph, and RAG Concepts

    0–8 min

    Spend time reading documentation and case studies to refresh your understanding of these frameworks and their use in production.

  2. Prepare Examples of AI Agent Projects

    8–15 min

    Select and outline 1-2 relevant projects where you built or integrated AI agents, focusing on challenges and outcomes.

  3. Brush Up on Scalable Application Design

    15–21 min

    Review best practices for building scalable, reliable applications in C#/.NET or Python, especially around APIs and integrations.

  4. Research MCP-based Tools and Integration Patterns

    21–26 min

    Look into MCP-based tools and how they are used in enterprise AI workflows.

  5. Draft Role-Specific Questions

    26–30 min

    Prepare thoughtful questions to ask the interviewer about the team, technology, and future projects.

Likely questions

, 6 items

Priority reflects how strongly this topic is emphasised in the job listing, not whether it will be asked.

Talking points

, 5 items

  • Building AI Agents and Agentic Workflows

    Demonstrate your experience designing and implementing AI agents, especially in enterprise or production environments.

  • Hands-on Experience with LangChain and LangGraph

    Showcase practical projects or solutions where you used these frameworks to orchestrate agent behavior and workflows.

  • Developing RAG-based Solutions

    Explain how you have built Retrieval-Augmented Generation (RAG) systems, particularly for enterprise data scenarios.

  • Software Engineering Fundamentals

    Highlight your expertise in APIs, integrations, and building scalable, reliable applications using C#/.NET or Python.

  • Experimentation and Iterative Development

    Provide examples of how you have iterated on solutions, experimented with new technologies, and refined products based on real-world feedback.

What to research

, 4 items

  • LangChain and LangGraph Documentation

    Review official documentation and recent updates for LangChain and LangGraph to ensure familiarity with their APIs and best practices.

  • RAG Architectures and Use Cases

    Study Retrieval-Augmented Generation (RAG) concepts, architectures, and real-world enterprise implementations.

  • C#/.NET and Python for Scalable Applications

    Refresh knowledge of building scalable, production-ready applications using C#/.NET or Python, focusing on APIs and integrations.

  • MCP-based Tools and Services

    Research MCP-based tools and how they are integrated into AI workflows and enterprise systems.

Questions to ask

, 6 items
  1. What are the main challenges the team is currently facing in bringing Agentic AI solutions to production?

    Why ask this? To understand the real-world problems you will help solve and where your expertise can have the most impact.

  2. How does the team approach experimentation and iteration when developing new AI workflows?

    Why ask this? To gauge the culture around innovation and continuous improvement.

  3. What is the current technology stack for AI agent orchestration, and how do LangChain and LangGraph fit in?

    Why ask this? To clarify the technical environment and your expected contributions.

  4. How is success measured for AI-driven products in this programme?

    Why ask this? To align your work with the team's goals and expectations.

  5. What opportunities are there for cross-functional collaboration with other teams or stakeholders?

    Why ask this? To understand how your work will interact with other parts of the organization.

  6. Are there any upcoming projects or initiatives involving new AI technologies or approaches?

    Why ask this? To learn about future directions and opportunities for growth.

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Apply NowApply before: 26 Oct 2026