AI/ML Computational Science Associate Manager

Accenture•Chicago, IL

About The Position

As an AI/ML Computational Scientist, you will design, build, and operationalize artificial intelligence and machine learning solutions for enterprise clients. You will combine custom models with cloud and third-party AI services to deliver production-ready outcomes. The role spans the solution lifecycle, from assessing client needs and data and selecting or customizing models—including Deep Learning, Generative AI, and Large Language Models—to designing scalable data and MLOps/LLMOps pipelines and ensuring the quality, value, and reliability of deployed systems. Travel may be required, ranging from 0% to 100% depending on client and business needs.

Requirements

At least 4 years of experience as a machine learning engineer or scientist deploying models in production at scale, including monitoring, alerting, automatic bug filing, and auditing.
At least 4 years of experience applying theoretical foundations of computer science, including computer system architecture, system engineering, and programming.
At least 4 years of experience in distributed computing systems and architecture, which may include big data, high-performance computing, engineering simulations, scientific computing, grid and cloud computing, or distributed networks.
At least 2 years of experience building and deploying AI/ML-based software to a cloud environment.
Bachelor’s degree or equivalent, with at least 12 years of work experience; alternatively, an associate degree with at least 6 years of work experience.

Responsibilities

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 and MLOps.
Customize and apply Deep Learning and Generative AI models for business use cases, considering data availability and system and infrastructure requirements, including edge devices and high-performance computing.
Engage in research and development of AI and high-performance computing algorithms, models, and simulations, and apply them to complex business problems at client sites.
Work with large-scale datasets and use data preprocessing techniques to ensure high-quality inputs 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 business and technical teams—including users, use case representatives, business owners, engineers, architects, and UI designers—to achieve end-to-end project goals and integrate solutions into production.
Travel as required by client and business needs, potentially from 0% to 100%.