Artificial Intelligence Engineer

Morgan McKinley; Cork; On-site; EUR 90,000 - 130,000; Full time

Job summary

Morgan McKinley seeks a Senior AI Systems Engineer & Architect to shape production-grade agentic architectures and scalable AI infrastructure in Cork, Ireland. This technical authority will bridge architectural vision with hands-on engineering across multi-agent workflows, memory systems, and enterprise LLMOps.

You will define standards, mentor teams, and partner with product and domain experts to drive robust, high-performance AI solutions while optimizing latency and cost.

Qualifications

• Proven track record architecting and deploying agentic systems in production.
• Strong foundation in distributed systems, TDD, DDD, and modern API design.
• Familiar with MCP, A2A, and agentic tooling.
• Experience with multimodal AI execution spanning text, voice, and vision is a plus.
• Technical leadership and mentoring capabilities.

Responsibilities

• Design agentic architectures and production-grade workflows.
• Build scalable context and memory systems (GraphRAG, LPG, RDF).
• Create reusable AI capabilities and plugins using MCP and A2A.
• Establish evaluation, observability, and debugging standards for AI lifecycle.
• Lead token economy, inference efficiency, and model routing strategies.
• Define roadmaps with Product, Architecture, and Domain experts.

Skills

Agentic systems

Multi-agent workflows

Context retrieval

Knowledge graphs

MCP

A2A

LLMOps

Observability

API design

Distributed systems

DDD

TDD

Mentoring

Performance optimization

GraphRAG

LPG

RDF

Tools

GraphRAG

LPG

RDF

Job description

Are you passionate about building state-of-the-art agentic systems, context architectures, and scalable AI infrastructure? We are seeking a Senior AI Systems Engineer & Architect to serve as a technical authority across our AI engineering initiatives.

In this role, you will bridge the gap between high‑level architectural vision and hands‑on engineering execution. You’ll lead the design and implementation of production‑grade multi‑agent frameworks, advanced context and memory systems, and enterprise LLMOps pipelines—setting the technical standards that empower engineering teams to build reliable, high‑performance AI solutions at scale.

What You’ll Do

• Design Next-Gen Agentic Architectures: Architect and deploy production‑ready multi‑agent workflows, establishing robust patterns for agent loops, tool execution, state management, retry strategies, and fail‑safe recovery.
• Advance Context & Knowledge Systems: Build scalable retrieval, memory, and knowledge graph architectures (including GraphRAG, LPG, and RDF) to optimize LLM reasoning and contextual awareness.
• Drive Interoperability & Integration: Establish standardized, reusable AI capabilities, skills, and plugins utilizing cutting‑edge protocols such as MCP, A2A, and custom tool‑calling frameworks.
• Elevate AI Reliability & LLMOps: Set up comprehensive evaluation frameworks, test harnesses, observability platforms, and production debugging standards across the entire AI lifecycle.
• Optimize System Performance: Lead strategies around token economics, inference efficiency, dynamic model routing, latency reduction, and cost optimization.
• Shape AI Engineering Standards: Partner cross‑functionally with Product, Architecture, and Domain Experts to define technology roadmaps and drive best practices across AI‑assisted SDLC and modern AI UX patterns (such as AG-UI and human‑in‑the‑loop workflows).

What We’re Looking For

• Deep AI Engineering Expertise: Proven track record of architecting and deploying agentic systems, multi‑agent orchestrations, and advanced RAG/retrieval frameworks in production.
• Core Software Engineering Rigor: Strong foundation in distributed systems, spec‑driven development, Test‑Driven Development (TDD), Domain‑Driven Design (DDD), and modern API design.
• Ecosystem Mastery: Deep familiarity with modern AI protocols and tools (e.g., Model Context Protocol (MCP), Agent‑to‑Agent (A2A), agentic coding tools, and evaluation frameworks).
• Multimodal Capabilities: Experience or strong interest in multimodal AI execution spanning text, voice, and vision systems.
• Technical Leadership: Natural ability to mentor engineering teams, influence technical direction, and establish reusable architectural patterns across complex environments.

Why Join Us?

• Cutting-Edge Tech Stack: Work directly on leading‑edge agentic AI, context engineering, and multimodal systems at the forefront of the industry.
• High-Impact Leadership: Own key technical decisions and set the benchmark for enterprise AI engineering standard methodologies.
• Collaborative Environment: Join a forward‑thinking culture that values software engineering excellence, innovation, and continuous learning.