About this role
About the Role Responsible AI Manager leads the full lifecycle of advanced AI research projects, analyzes complex business and technical problems, defines objectives, and directs multidisciplinary teams to design and implement innovative solutions. The role provides strategic leadership to Accenture's Responsible AI R&D efforts, applying theoretical expertise and cutting-edge methods to adapt AI technologies and address emerging challenges. It also focuses on ethical standards, policy development, training, and staying current with AI trends, with opportunities to contribute to publications and conferences. What You'll Do
- Lead and oversee the full lifecycle of advanced AI research projects, from problem framing to solution design and implementation.
- Provide strategic leadership and guidance to the Responsible AI R&D team.
- Apply theoretical expertise and cutting-edge research to develop or adapt AI technologies using state-of-the-art methods and open-source tools.
- Optimize and maintain existing AI systems for performance and ethical compliance.
- Develop organizational policies, guidelines, and best practices for Responsible AI; design technical training programs for traditional and generative AI applications.
- Stay current with AI trends, ethics, and technology advances; contribute to internal and external publications; speak at conferences. What We're Looking For
- Bachelor's degree in Computer Science, Technology, Computer Information Systems, Computer Applications, Engineering, or related field.
- 5 years of progressive post-baccalaureate experience in the IT consulting industry.
- 5 years of hands-on experience in each of the following areas: developing and deploying ML models and AI/ML solutions using DL, NLP, and CV; programming in Python, R, or Java; ML frameworks such as TensorFlow, PyTorch, Scikit-learn; big data technologies (Hadoop, Spark); database management (Oracle, PostgreSQL); cloud platforms (AWS, Azure, Google Cloud); rapid API development with Django, Flask, FastAPI; version control with Gitlab/InnerSource; deploying and integrating API endpoints via Docker and Kubernetes.
- Willingness and ability to travel domestically ~80% of the time.