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AI/ML Solutions Lead

Ireland, Dublin · Job Posted May 05, 2026
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Job Description

Enterprise AI is maturing fast. Clients are moving past proof-of-concept and into production systems that need to be reliable, scalable, and genuinely useful. Accenture's AI and Data practice in Ireland is at the centre of that work, and this role sits within the delivery function making it happen. For an engineer who wants client exposure, technical breadth, and the scope to build something worth building, this is a strong position. The AI/ML Solutions Lead owns the technical delivery of AI solutions for some of Ireland's most complex organisations. The scope is broad: agentic systems, LLM-powered applications, foundational model development, fine-tuning, and production ML across computer vision, time-series, and other disciplines. The role carries team leadership responsibility. You will manage a group of AI/ML engineers, set technical standards, and be accountable for delivery quality across engagements. Beyond delivery, this role contributes to how the practice builds AI. That includes shaping methodology, developing reusable assets, and supporting business development where relevant.

Job Responsibility

  • Lead AI solution delivery across the full lifecycle, from architecture and build through to production deployment
  • design and build multi-agent AI systems
  • architect LLM-powered applications: RAG pipelines, tool-augmented agents, and memory-enabled systems
  • build production-grade agentic infrastructure
  • manage and develop a team of AI/ML engineers
  • define safety boundaries, escalation logic, and audit mechanisms for autonomous systems
  • work with AI Solution Architects and Data Architects to translate client requirements into deployable technical plans
  • contribute to practice development
  • support business development through proposal input, client demonstrations, and building senior relationships

Requirements

  • Agentic systems
  • LLM-powered applications
  • foundational model development
  • fine-tuning
  • production ML across computer vision, time-series, and other disciplines
  • team leadership
  • architecture and build through production deployment
  • multi-agent AI systems using established frameworks or custom orchestration
  • RAG pipelines
  • tool-augmented agents
  • memory-enabled systems
  • production-grade agentic infrastructure (state management, task decomposition, human-in-the-loop controls, guardrails, observability)
  • define safety boundaries, escalation logic, and audit mechanisms for autonomous systems in regulated environments
  • work with AI Solution Architects and Data Architects
  • contribute to practice development (frameworks, accelerators, reusable solution patterns)
  • support business development through proposal input, client demonstrations, and building senior relationships

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