Senior AI/ML Solutions Architect – Production-Ready Systems

Accenture; Dublin; On-site; EUR 85,000 - 125,000; Full time

Job summary

Accenture in Ireland is seeking an AI/ML Computational Scientist to design, build, and operationalize AI and machine learning solutions for enterprise clients. You'll work across model selection (including Deep Learning, Gen AI, and LLMs), data prep, and scalable DevOps & MLOps pipelines to deliver production-ready outcomes.

You will collaborate with business and technical teams, adapt models to client data and infrastructure, and ensure quality, value, and reliability of deployed systems in

Qualifications

• Proven experience as a machine learning engineer or scientist deploying models in production at scale.
• Experience applying theory of computer science, architectures, and programming.
• Experience in distributed computing and cloud-based AI/ML deployments.

Responsibilities

• Formulate real-world problems into scalable AI/ML solutions.
• Develop algorithms, models, and data pipelines for training and production with DevOps & MLOps.
• Customize Deep Learning and Gen AI models for client needs and edge/HPC use cases.
• Engage in R&D of new AI algorithms and simulations for client applications.
• Work with large datasets and ensure high-quality input for training.
• Implement and maintain data storage and retrieval for models and knowledge.
• Justify model approaches in business contexts.
• Collaborate with business and technical teams to achieve end-to-end goals.

Skills

Machine Learning

Distributed computing

Cloud environments

Python

Education

Bachelor's degree

Tools

TensorFlow

PyTorch

Job description

Accenture in Ireland is seeking an AI/ML Computational Scientist to design, build, and operationalize AI and machine learning solutions for enterprise clients. You'll work across model selection (including Deep Learning, Gen AI, and LLMs), data prep, and scalable DevOps & MLOps pipelines to deliver production-ready outcomes.

You will collaborate with business and technical teams, adapt models to client data and infrastructure, and ensure quality, value, and reliability of deployed systems in