AI Engineer – System Z Mainframe & LLM Infra

International Business Machines Corporation; Waterford; On-site; EUR 42,000 - 65,000; Full time

Job summary

IBM Ireland in Waterford is seeking an entry-level AI Engineer to join the System Z mainframe team, bringing AI capabilities to enterprise systems that process billions of transactions.

You will work with Spyre hardware accelerators, spanning from firmware interfaces to application APIs, collaborating with senior engineers to determine model loading, inference optimisation, and resource allocation for enterprise-scale workloads.

Qualifications

• Strong Python programming skills with some experience in C/C++ and Linux-based development environments.
• Understanding of machine learning fundamentals, particularly neural networks and transformer-based models, alongside an interest in AI inference and performance optimisation.
• Basic knowledge of computer architecture, including memory hierarchies, parallel processing and I/O systems, with an interest in profiling and performance analysis.
• Strong problem-solving, debugging and analytical skills, with good technical communication and documentation abilities.
• A collaborative mindset, curiosity about complex systems and enthusiasm for learning from experienced engineers in AI profiling and hardware acceleration.

Responsibilities

• LLM Integration and Deployment on System Z.
• Performance Profiling and Optimisation.
• Failure Analysis and Debugging.
• Observability and Telemetry Infrastructure.
• Cross-Stack Collaboration.
• Learning and Growth.

Skills

Python

C/C++

Linux

ML fundamentals

Profiling

Tools

PyTorch

TensorFlow

Docker

Kubernetes

Job description

IBM Ireland in Waterford is seeking an entry-level AI Engineer to join the System Z mainframe team, bringing AI capabilities to enterprise systems that process billions of transactions.

You will work with Spyre hardware accelerators, spanning from firmware interfaces to application APIs, collaborating with senior engineers to determine model loading, inference optimisation, and resource allocation for enterprise-scale workloads.