Responsible AI Engineer Specialist

Accenture•Chicago, IL

About The Position

Accenture’s Global Responsible AI team within the Global Data & AI Practice helps organizations design, build, and deploy AI in ways that foster trust and adhere to laws, regulations, and ethical norms. The team develops Responsible AI intellectual property and solutions with ecosystem partners, engages with regulators on policy, conducts research with academia, and provides client training and resources through the Responsible AI Academy. The Responsible AI Engineer develops state-of-the-art AI models and solutions for challenging business problems while applying responsible AI principles. The role evaluates and addresses dimensions such as fairness, transparency, explainability, robustness, soundness, and privacy, and contributes to client engagements, sales conversations, proposals, and internal responsible AI practices. The position involves the full AI development lifecycle, from data preparation and design through coding, testing, deployment, and support. Travel may be required and can range from 0% to 100%, depending on business and client needs.

Requirements

Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field, or equivalent work experience of at least 12 years. With an associate’s degree, at least 6 years of work experience is required.
At least 3 years of experience developing and evaluating machine learning models and AI solutions.
Experience in at least two of the following areas: statistics and probability theory; regression modelling; clustering methods; time-series forecasting; tree ensembles; deep learning; neural networks; generative AI; natural language processing; text mining; computer vision; image recognition; machine learning visualization tools.
At least 3 years of experience with programming languages such as Python, R, or Java.
At least 3 years of experience with AI and machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch.
At least 3 years of experience with rapid API development using frameworks and tools such as Django, Flask, or FastAPI.
At least 3 years of experience with fundamental version control practices and platforms such as GitHub or Bitbucket.
At least 3 years of experience with database management and data processing.
At least 3 years of experience with cloud services and platforms related to AI, such as AWS, Azure, or Google Cloud.
At least 3 years of experience working with complex datasets and performing data preprocessing and analysis.
Strong problem-solving and analytical skills.
Ability to collaborate in a team and communicate effectively with team members.
Commitment to ethical AI development and continuous learning.
Ability to manage multiple projects and priorities in a fast-paced environment.
Excellent documentation and presentation skills for technical and non-technical audiences.
Applicants for US employment must have work authorization that does not now or in the future require visa sponsorship.

Nice To Haves

Managerial experience leading teams.
Experience with big data technologies and tools such as Hadoop or Spark.
At least one year of experience addressing responsible AI aspects such as fairness, transparency, explainability, robustness, soundness, and privacy, preferably in a client-facing environment.

Responsibilities

Develop and implement AI models and solutions using ethical and responsible practices.
Detect, evaluate, and apply relevant Responsible AI dimensions, including bias, fairness, robustness, and explainability, and help mitigate identified risks.
Support client sales conversations and proposal preparation.
Optimize and maintain existing AI systems to improve performance and adherence to ethical standards.
Participate in the full AI development lifecycle, including data preparation, design, coding, testing, deployment, and support.
Contribute to internal guidelines, best practices for responsible AI development, and technical training.
Stay current with AI trends, ethical considerations, and technological advancements.
Assist in identifying and correcting biases in datasets and AI models.
Document AI development processes and maintain transparent records of decisions and methodologies.
Travel as needed based on business needs and client requirements; travel may range from 0% to 100%.