AI/ML Computational Science Manager

Role Summary We are looking for an experienced AI/ML Engineering Manager to lead the design, development, and deployment of scalable AI and machine learning solutions. You will turn business needs into production-grade AI systems, using cloud AI services, deep learning, and Generative AI across cloud, edge, and High Performance Computing (HPC) environments. You will also lead R&D into new algorithms and models to solve complex problems. Key Responsibilities • Design and develop AI and ML systems using existing cloud AI services and platforms. • Architect and build scalable data pipelines for model training and production, applying DevOps and MLOps best practices. • Customize and apply Deep Learning and Generative AI models to business use cases, based on data availability and system and infrastructure requirements, including edge devices and HPCs. • Evaluate and justify the quality, performance, and business value of AI solutions to stakeholders. • Lead research and development of new AI and high-performance compute algorithms, models, and simulations, and apply them to complex business problems. • Work with large-scale datasets and apply 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 bases using appropriate tools. • Lead, mentor, and develop a team of AI/ML engineers and data scientists, setting technical direction and engineering standards. • Partner with clients and business stakeholders to identify AI opportunities, shape solutions, and drive delivery from concept to production. Required Qualifications • 10+ years of experience in AI/ML engineering, data science, or software engineering, including proven experience leading technical teams. • Hands-on experience with at least one major cloud AI platform (e.g., Azure AI / Azure ML, AWS SageMaker / Bedrock, Google Vertex AI). • Strong programming skills in Python and deep learning frameworks such as PyTorch or TensorFlow. • Solid expertise in Deep Learning and Generative AI, including LLMs, fine-tuning, Retrieval-Augmented Generation (RAG), and prompt engineering. • Strong MLOps and DevOps experience: CI/CD, Docker, Kubernetes, MLflow or Kubeflow, and model monitoring in production. • Experience building large-scale data pipelines with tools such as Spark, Databricks, Airflow, or Kafka. • Experience with data storage and retrieval technologies, including SQL/NoSQL databases, data lakes, and vector databases (e.g., FAISS, Milvus, Pinecone). • Experience optimizing and deploying models on edge devices (e.g., ONNX, TensorRT, quantization) and/or HPC environments (e.g., CUDA, distributed training). • Ability to communicate technical solutions and their business value clearly to senior stakeholders. Preferred Qualifications • Master's or PhD in Computer Science, Artificial Intelligence, Data Science, Applied Mathematics, or a related field. • Track record in AI or HPC research, such as publications, patents, or simulation work. • Relevant cloud AI certifications (Azure, AWS, or Google Cloud). • Consulting or client-facing delivery experience. Education Bachelor's degree in Computer Science, Engineering, or a related field. About Accenture Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities. Visit us at www.accenture.co

10+ years of experience in AI/ML engineering, data science, or software engineering, including proven experience leading technical teams. Hands-on experience with at least one major cloud AI platform (e.g., Azure AI / Azure ML, AWS SageMaker / Bedrock, Google Vertex AI). Strong programming skills in Python and deep learning frameworks such as PyTorch or TensorFlow. Solid expertise in Deep Learning and Generative AI, including LLMs, fine-tuning, Retrieval-Augmented Generation (RAG), and prompt engineering. Strong MLOps and DevOps experience: CI/CD, Docker, Kubernetes, MLflow or Kubeflow, and model monitoring in production. Experience building large-scale data pipelines with tools such as Spark, Databricks, Airflow, or Kafka. Experience with data storage and retrieval technologies, including SQL/NoSQL databases, data lakes, and vector databases (e.g., FAISS, Milvus, Pinecone). Experience optimizing and deploying models on edge devices (e.g., ONNX, TensorRT, quantization) and/or HPC environments (e.g., CUDA, distributed training). Ability to communicate technical solutions and their business value clearly to senior stakeholders.

Design and develop AI and ML systems using existing cloud AI services and platforms. Architect and build scalable data pipelines for model training and production, applying DevOps and MLOps best practices. Customize and apply Deep Learning and Generative AI models to business use cases, based on data availability and system and infrastructure requirements, including edge devices and HPCs. Evaluate and justify the quality, performance, and business value of AI solutions to stakeholders. Lead research and development of new AI and high-performance compute algorithms, models, and simulations, and apply them to complex business problems. Work with large-scale datasets and apply 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 bases using appropriate tools. Lead, mentor, and develop a team of AI/ML engineers and data scientists, setting technical direction and engineering standards. Partner with clients and business stakeholders to identify AI opportunities, shape solutions, and drive delivery from concept to production.