AI Engineer - Contract

Morgan McKinley; Cork; In loco; EUR 120.000 - 160.000; Tempo pieno

Descrizione del lavoro

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.

Competenze

• 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.

Mansioni

• 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.

Conoscenze

Agentic systems

Multi-agent orchestrations

RAG retrieval frameworks

Distributed systems

TDD

DDD

API design

A2A protocols

Evaluation frameworks

Multimodal AI

Strumenti

MCP

A2A

GraphRAG

LPG

RDF

Descrizione del lavoro

About the Role

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.