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Ai modelling engineer | contract

Dublin
Morgan Mckinley
Modelling engineer
Posted: 23 October
Offer description

Morgan McKinley is excited to partner with a leading technology conglomerate organisation to hire for an AI Modelling Engineer. The client's research and development division is expanding and they are seeking an AI Modelling Engineer to join their team who will design, develop, and deploy next-generation AI systems with autonomous, goal-driven capabilities.
This is initially a 1-year contract with annual review for extension.
Please note this is an onsite role in Dublin.
This role focuses on building digital twin models for AI agents that can reason, plan, act, and interact within complex environment representations—decision-making frameworks, and knowledge graphs to deliver intelligent, adaptive solutions.
About the Role
The successful candidate will investigate and perform research work on Autonomous Network topics. More specifically, they will:

Identify the key technologies to enable the autonomous network
Apply state-of-the-art solutions to build a small‑scale POC on the topic building Agentic AI solutions
Work with the domain experts and other researchers to prepare deep‑dive reports

Requirements

Master’s or PhD in Computer Science, AI/ML, Data Science, or related field (or equivalent industry experience)
Strong programming skills in Python (plus experience with frameworks such as PyTorch, TensorFlow, LangChain, AutoGen, or similar)
3+ years hands‑on experience with building and finetuning AI models (preferably LLMs)
Advanced knowledge of statistical and algorithmic models as well as of fundamental mathematical concepts, such as linear algebra and probability

Preferred

Knowledge of RAG, semantic reasoning, or symbolic AI
Familiarity with AI training infrastructures and software stack
Contributions to open‑source agentic AI frameworks or research publications

If this sounds like you, apply today for further information.
Referrals increase your chances of interviewing at Morgan McKinley by 2x.
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