
at Accenture
ConsultanciesPosted 3 days ago
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AI/ML Computational Science Manager designs, builds, and deploys AI/ML solutions for enterprise clients. Manages full lifecycle from assessing needs to delivering production-ready outcomes. Essential skills include full-stack ML (Deep Learning, Generative AI), MLOps/LLMOps, DevOps, data preprocessing, and collaboration with cross-functional teams. 5+ years of relevant experience required.
- Compensation
- Not specified
- City
- Not specified
- Country
- United Kingdom
Currency: Not specified
Full 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 MLOps/LLMOps pipelines for training and production, and ensuring quality, value, and reliability of deployed systems.
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




