Job Description

YOU ARE 

As an AI/ML Computational Scientist, you will design, build, and operationalize artificial intelligence and machine learning solutions for enterprise clients, combining custom models with cloud and third-party AI services to deliver production-ready outcomes. Your role spans the full solution lifecycle — assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and Large Language Models), designing scalable data and DevOps & MLOps pipelines for training and production, and ensuring quality, value, and reliability of deployed systems. 

Benefits:

• Compensation at Accenture varies depending on a wide array of factors including but not limited to role, level, location, effort, responsibility, skillset, and level of experience. The range of base pay for this role is 1,900 - 4,700 EUR gross per month
• Flexible vacation + health & travel insurance + relocation
• Work from home, flexible working hours
• Work with Fortune 500 companies from different industries all over the world
• Skills development and training opportunities, company-paid certifications
• Opportunities to advance career
• An open-minded and inclusive company culture

THE WORK 

• Formulate real-world problems into practical, efficient, and scalable AI and Machine Learning solutions 
• Develop and implement machine learning algorithms, models, and computational systems; design and build scalable data pipelines to support model training and production with DevOps & MLOps 
• Customize and apply Deep Learning and Gen AI models for various use cases based on the business needs, data availability, system and infrastructure requirements - including edge device and HPC 
• Engage in research and development of new AI and high-performance compute algorithms, models, and simulations along with their applications to solve complex business problems at client sites 
• Work with large-scale datasets and utilize data preprocessing techniques to ensure high-quality input for training and production 
• Implement and maintain efficient data storage and retrieval mechanisms for models and knowledge using appropriate tools 
• Justify the value of model approaches in business problems 
• Collaborate with teams from both business and technical sides, including users, use case representatives, business owners, engineers, architects, and UI designers, to achieve end-to-end project goals and integrate into production 
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