AI Engineer

Company Information and Introduction:

Digital Manufacturing Ireland (DMI) is an industry-led organisation, supported by the Government of Ireland through IDA Ireland. Launched in 2023, DMI enables Irish-based manufacturers to access, adopt, and accelerate new digital technologies—solving real-world challenges and driving future competitiveness.

DMI offers a state-of-the-art physical and digital factory, a vendor showcase, industry collaboration spaces, and training facilities. The DMI facility brings together technology, expertise, and business support to help manufacturing companies transform, innovate, and future-proof their operations.

Role Overview:

The AI Engineer will design, build and deploy practical AI solutions that help manufacturing organisations solve real operational challenges. The role will focus on applied generative \& Agentic AI, including retrieval-augmented generation (RAG), AI assistants, agentic workflows and multimodal applications that connect securely with enterprise and manufacturing data.

Working with manufacturing subject-matter experts and technology partners, this role will take use cases from discovery and rapid prototyping through evaluation and production deployment. This is a hands-on role for someone who combines strong software engineering with

Key Responsibilities and Duties:

Applied Generative AI

• Build production-ready AI applications including knowledge assistants, copilots and multimodal solutions.
• Design RAG solutions over manufacturing content such as SOPs, OCAPs, FMEAs, CAPAs and equipment manuals, producing grounded and traceable responses.
• Develop agentic workflows using tool calling, structured outputs and human approval steps where they add clear operational value.

AI Engineering \& Integration

• Develop APIs, services and reusable components that integrate AI models with enterprise and manufacturing systems, databases and workflows.
• Evaluate and select models, retrieval approaches and AI services based on quality, security, latency, cost, maintainability and user needs.

Production Delivery \& Responsible AI

• Deploy and operate AI services in AWS, Azure or GCP using appropriate containerisation, version control and CI/CD practices.
• Build evaluation and observability into AI solutions, monitoring response quality, groundedness, safety, performance, cost and user feedback.
• Apply guardrails, access controls, auditability, data-protection practices and human oversight suitable for industrial and regulated settings.

Manufacturing AI \& Collaboration

• Work on use cases such as yield analytics, anomaly detection, predictive maintenance and computer vision, integrating model outputs into usable applications and workflows.
• Collaborate with subject-matter experts to frame use cases, understand structured and unstructured industrial data, define success measures and evaluate solutions against operational KPIs.
• Keeps current with the evolving GenAI/agentic landscape and evaluates emerging models, frameworks and tools for practical fit.

Key Skills and Competencies: Skills \& Experience Required:

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering or a related discipline - or equivalent practical experience.
• 2+ years of relevant experience in AI engineering, software engineering, applied AI, data science or a comparable hands-on role.
• Strong Python skills and sound software-engineering practices, including APIs, testing, version control and maintainable application design.
• Practical experience building with LLM APIs or open-source models, including RAG, embeddings, vector search, structured outputs or agentic workflows.
• Experience integrating AI applications with databases, documents, APIs and operational systems.
• Familiarity with AWS, Azure or GCP, Docker and CI/CD fundamentals.
• Experience evaluating and monitoring AI systems for quality, reliability and safety.
• Able to translate operational needs into practical AI solutions with non-technical stakeholders and subject-matter experts.

Desirable Experience:

• Manufacturing, industrial or IoT environments and data sources, including MES, SCADA, historians, sensors, event logs or industrial image data.
• Machine learning or computer vision using TensorFlow, PyTorch or scikit-learn; exposure to industrial inspection, edge AI, digital twins, simulation or reinforcement learning
• Experience in regulated or manufacturing sectors such as medtech, pharma, food or discrete manufacturing.