AI Platform Engineer: Scalable ML Infra & Deployment

ActAI; Ireland; On-site; EUR 90,000 - 130,000; Full time

Job summary

ActAI in Ireland is hiring an ML Platform Engineer to build the infrastructure powering our AI capabilities. You will design and operate systems for model training, evaluation, deployment, inference, observability, and continuous improvement.

You'll collaborate with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure, focusing on reliability, scalability, and cost efficiency across the stack.

Qualifications

• Strong software engineering fundamentals and production systems experience.
• Experience building ML infrastructure, platforms, or production machine learning systems.
• Experience with model deployment, inference, evaluation, or data pipelines.
• Strong understanding of distributed systems and system reliability.
• Ability to write clean, maintainable, production-quality code.

Responsibilities

• Build and operate the ML infrastructure powering AI products.
• Design systems for model training, evaluation, deployment, inference, and experimentation.
• Build and optimise model serving and inference infrastructure for high-throughput and low-latency workloads.
• Improve reliability, scalability, latency, and cost efficiency of AI systems.
• Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement.
• Build platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster.
• Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions.

Skills

Python

PyTorch/JAX

LLM Serving

Cloud infrastructure

Distributed systems

ML pipelines

GPU tooling

Vector databases

Tools

vLLM

SGLang

TensorRT-LLM

Job description

ActAI in Ireland is hiring an ML Platform Engineer to build the infrastructure powering our AI capabilities. You will design and operate systems for model training, evaluation, deployment, inference, observability, and continuous improvement.

You'll collaborate with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure, focusing on reliability, scalability, and cost efficiency across the stack.