Artificial Intelligence Engineer

Fruition Group Ireland; Leinster; On-site; EUR 70,000 - 110,000; Full time

Job summary

Fruition Group Ireland is seeking an AI Engineer to design, develop, and deploy AI-powered applications and data-driven solutions. The role involves building AI agents, RAG workflows, and production-ready services using LLMs and MCP APIs.

You will collaborate with software and data teams, ensure security and governance, and help translate business needs into scalable AI products. The role is mostly onsite with a permanent contract in Co Westmeath, Ireland.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Software Engineering, or a related discipline.
• 3+ years of experience in software engineering, data engineering, or AI/ML development with experience delivering production solutions.
• Strong Python development skills with experience integrating LLM APIs such as OpenAI, Azure OpenAI, Anthropic Claude, or similar.
• Experience with AI frameworks including LangChain, LangGraph, LlamaIndex, or equivalent.
• Knowledge of Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), vector databases, embeddings, and prompt engineering.
• Experience with Azure AI services or other cloud AI platforms.
• Understanding of MLOps/LLMOps practices, Docker, CI/CD pipelines, and AI monitoring and evaluation.
• Strong understanding of AI security, governance, privacy, and responsible AI principles.
• Ability to translate business requirements into practical AI solutions and communicate effectively with both technical and non-technical stakeholders.

Responsibilities

• Design, develop, and maintain AI agents and agentic workflows to automate business processes and data-driven tasks.
• Build AI applications using LLMs, integrating external models and services while optimising prompts, context management, performance, and cost.
• Develop Retrieval-Augmented Generation (RAG) solutions, vector databases, and AI-powered data pipelines that combine structured and unstructured data.
• Build integrations using Model Context Protocol (MCP) and APIs to connect AI systems with enterprise applications, databases, and third-party services.
• Design, deploy, and maintain production-ready AI services, including monitoring, testing, observability, and performance optimisation.
• Implement AI evaluation frameworks to measure response quality, accuracy, latency, reliability, and cost efficiency.
• Apply MLOps/LLMOps best practices including version control, CI/CD, containerisation, and automated deployment.
• Ensure AI solutions meet security, governance, privacy, and responsible AI standards.
• Collaborate with software engineers, data engineers, data scientists, and business stakeholders to deliver AI-driven solutions.
• Produce and maintain technical documentation covering architecture, integrations, deployment, and operational processes.

Skills

Python development

LLM integration

MLOps/LLMOps

AI security & governance

Data engineering

Stakeholder communication

Production AI systems

Education

Bachelor's degree in Computer Science, Data Science, Software Engineering, or a related discipline

Tools

LangChain

LangGraph

LlamaIndex

Model Context Protocol (MCP)

Vector databases

Docker

CI/CD

Job description

My client based in Co Westmeath is currently recruiting for an AI Engineer to join a growing company, this is a permanent role and mostly onsite. The AI Engineer is responsible for designing, developing, and implementing AI-powered applications, intelligent agents, and data-driven solutions that support automation and informed decision-making. This role combines software engineering, data engineering, and applied AI expertise to create scalable, secure, and production-ready solutions using large language models (LLMs), retrieval-augmented generation (RAG), and modern AI frameworks.

Responsibilities

• Design, develop, and maintain AI agents and agentic workflows to automate business processes and data-driven tasks.
• Build AI applications using LLMs, integrating external models and services while optimising prompts, context management, performance, and cost.
• Develop Retrieval-Augmented Generation (RAG) solutions, vector databases, and AI-powered data pipelines that combine structured and unstructured data.
• Build integrations using Model Context Protocol (MCP) and APIs to connect AI systems with enterprise applications, databases, and third-party services.
• Design, deploy, and maintain production-ready AI services, including monitoring, testing, observability, and performance optimisation.
• Implement AI evaluation frameworks to measure response quality, accuracy, latency, reliability, and cost efficiency.
• Apply MLOps/LLMOps best practices including version control, CI/CD, containerisation, and automated deployment.
• Ensure AI solutions meet security, governance, privacy, and responsible AI standards.
• Collaborate with software engineers, data engineers, data scientists, and business stakeholders to deliver AI-driven solutions.
• Produce and maintain technical documentation covering architecture, integrations, deployment, and operational processes.

Skills & Experience

• Bachelor's degree in Computer Science, Data Science, Software Engineering, or a related discipline.
• 3+ years of experience in software engineering, data engineering, or AI/ML development with experience delivering production solutions.
• Strong Python development skills with experience integrating LLM APIs such as OpenAI, Azure OpenAI, Anthropic Claude, or similar.
• Experience with AI frameworks including LangChain, LangGraph, LlamaIndex, or equivalent.
• Knowledge of Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), vector databases, embeddings, and prompt engineering.
• Experience with Azure AI services or other cloud AI platforms.
• Understanding of MLOps/LLMOps practices, Docker, CI/CD pipelines, and AI monitoring and evaluation.
• Strong understanding of AI security, governance, privacy, and responsible AI principles.
• Ability to translate business requirements into practical AI solutions and communicate effectively with both technical and non-technical stakeholders.