Senior AI Systems Architect - Multi-Agent & Context Engines

Morgan McKinley; Cork; On-site; EUR 120,000 - 160,000; Full time

Job summary

Morgan McKinley in Ireland seeks a Senior AI Systems Engineer & Architect to lead design and implementation of production-grade multi-agent frameworks, context and memory systems, and LLMOps pipelines.

You will bridge architectural vision and hands-on engineering, setting standards, driving performance, reliability, and scalability, and partnering with product and domain experts to shape AI engineering practices.

Qualifications

• Proven track record architecting agentic systems in production.
• Strong foundation in distributed systems, TDD, DDD, and modern API design.
• Familiar with MCP, A2A, evaluation frameworks.
• Interest or experience with multimodal AI execution.

Responsibilities

• Design Next-Gen Agentic Architectures: Architect and deploy production-ready multi-agent workflows with robust patterns and fail-safe recovery.
• Advance Context & Knowledge Systems: Build scalable retrieval, memory, and knowledge graphs for optimized LLM reasoning.
• Drive Interoperability & Integration: Establish reusable AI capabilities, plugins, and protocols across tools.
• Elevate AI Reliability & LLMOps: Set up evaluation, testing, observability, and production debugging standards.
• Optimize System Performance: Lead token economy, inference efficiency, dynamic routing, latency, and cost strategies.
• Shape AI Engineering Standards: Collaborate with Product/Architecture to define AI SDLC roadmaps and best practices.

Skills

Agentic systems

Multi-agent orchestrations

RAG retrieval frameworks

Distributed systems

TDD

DDD

API design

A2A protocols

Evaluation frameworks

Multimodal AI

Tools

MCP

A2A

GraphRAG

LPG

RDF

Job description

Morgan McKinley in Ireland seeks a Senior AI Systems Engineer & Architect to lead design and implementation of production-grade multi-agent frameworks, context and memory systems, and LLMOps pipelines.

You will bridge architectural vision and hands-on engineering, setting standards, driving performance, reliability, and scalability, and partnering with product and domain experts to shape AI engineering practices.