Company Information and Introduction:
Digital Manufacturing Ireland (DMI):
A Strategic Government Initiative, led by the IDA, DMI is an institute which is intended to help Irish based manufacturers (MNC's and SME's) to access and accelerate their adoption of digital technologies. Offering a world class physical and digital factory, a vendor showcase, an industry collaboration space and training facilities the institute will bring together technology, expertise, training and business supports to help manufacturing companies to enhance, transform and position their operations for the future.
Location:
DMI is located in the National Technology Park, Limerick, Ireland. This role is a flexible working model. Employees can work fully at the DMI HQ or in a hybrid approach of partly remote and partly at the DMI HQ.
Role overview:
As a graduate member of DMI's Data and Digital Technology group, the Graduate AI Engineer will work alongside DMI's AI Architect and senior engineers to build, test and deploy machine learning and generative AI solutions for real manufacturing environments — from early prototypes and demonstrators through to client pilots.
This is a hands-on role for a curious, motivated graduate: someone who enjoys working with data, code, models and APIs, and wants to see their work running on a factory floor rather than staying in a notebook.
The successful candidate will contribute to AI applications across DMI's focus areas, including AI-enabled quality, predictive maintenance, production planning, digital assistants and copilots, operator support, computer vision, and agentic workflow automation — with structured mentorship and a clear development path at every step.
This role will contribute towards DMI's long-term success as Ireland's national destination for advanced manufacturing and Industry 5.0 supports, ensuring Ireland's manufacturing base and supporting ecosystem remains resilient, sustainable, human-centric and competitive into the future.
Key Responsibilities and Duties:
Main responsibilities:
Develop and test machine learning models (classification, forecasting, anomaly detection, time-series analytics) on industrial datasets, under the guidance of senior team members.
Build and iterate on generative AI applications — retrieval-augmented generation (RAG), LLM-based assistants and copilots — including prompt design, evaluation and guardrails.
Assist in building data pipelines that connect shop-floor (OT), enterprise and cloud data sources for AI use cases.
Contribute to DMI's reusable AI accelerators, demonstrators and reference implementations for manufacturing use cases.
Support client pilots and proof-of-concept projects: data preparation, model experimentation, testing, documentation and results presentation.
Apply modern AI engineering tools and frameworks (e.g. Python, PyTorch or TensorFlow, scikit-learn, LangChain, LlamaIndex, vector databases, MCP or equivalent) with growing independence.
Learn and apply responsible AI practices — data quality, model evaluation, AI governance, security and human-in-the-loop design — under DMI's governance frameworks (EU AI Act, ISO 42001, GDPR).
Contribute to DMI training content and workshops, helping to build AI capability across Ireland's manufacturing ecosystem.
You can look forward to
Structured mentorship from DMI's AI Architect and senior engineers, with a defined graduate development path.
Building practical AI solutions that create measurable value in real manufacturing environments — not just experiments.
Hands-on exposure to the full AI lifecycle: data, models, deployment, monitoring and adoption.
Working with cutting-edge ML and generative AI tooling in a purpose-built digital factory environment.
Training, certifications and conference opportunities to accelerate your professional growth.
Key Skills and Competencies:
Experience Required
A bachelor's or master's degree (achieved or expected) in computer science, artificial intelligence, data science, engineering, mathematics or a related technical field.
Strong programming foundation in Python, with exposure to machine learning libraries (e.g. scikit-learn, PyTorch, TensorFlow) through coursework, projects or internships.
Understanding of generative AI concepts — LLMs, prompting, RAG — demonstrated through academic work, personal projects or internships.
Familiarity with data handling fundamentals: SQL, data preparation, and version control (Git).
Clear communicator who can explain technical work to non-technical audiences and collaborate well in a team.
Curious, enthusiastic and resourceful self-starter, eager to learn manufacturing domain knowledge and grow with structured feedback.
No prior industry experience required — final-year projects, internships, hackathons and open-source contributions all count.
