About the Job
We are Accenture’s Global Responsible AI team within the Global Data & AI Practice. AI is becoming increasingly pervasive, powerful, and accessible, presenting both new opportunities and significant risks. Our mission is to work with leading organizations to ensure that AI is designed, built, and deployed in a manner that engenders trust and adheres to laws, regulations, and ethical norms. Our Responsible AI strategy aims to embed responsibility into all of Accenture’s data and AI activities. This includes developing and deploying differentiated intellectual property and Responsible AI solutions with our ecosystem partners, engaging regulators to shape the policy agenda, conducting pioneering research with academia, and offering training and resources to our clients through the Responsible AI Academy. Our goal is to help clients turn AI risks into opportunities.
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The Role
We are looking to expand our Responsible AI team, and if you have experience in Monitoring & Compliance with a focus on Responsible AI, we would love to hear from you. As a Responsible AI Monitoring & Compliance professional, you will ensure that AI systems are developed and maintained to mitigate associated risks. You should have at least a year’s experience in responsible aspects such as AI model fairness, transparency, explainability, robustness, soundness, and privacy. This experience is ideally obtained in a client-facing environment.
You will bring diverse expertise in monitoring AI systems, machine learning model risk management, and applying regulatory requirements to frameworks and technical tools. Strong communication skills will be essential as you liaise with clients and colleagues.
At the forefront of the industry, your contribution will help make our Responsible AI vision a reality for clients looking to enhance customer service and create always-on enterprises. Our focus is not just on increasing revenue but also on ensuring that our technologies benefit millions of lives responsibly and inclusively.
Key Responsibilities
- Lead the architecture and design of ethical AI systems, ensuring compliance with relevant regulations and organizational policies.
- Support client sales conversations and proposal preparation.
- Collaborate with cross-functional teams to design integrations with existing data & AI platforms and workflows on the cloud.
- Guide best practices and architectural guidelines for AI model development, testing, deployment, and monitoring.
- Architect regular audits of AI systems to identify and address biases, ensuring fairness and transparency.
- Provide technical leadership and mentorship to AI development teams and deliver Responsible AI technical training.
- Facilitate communication between technical and non-technical stakeholders regarding AI projects and their impacts.
- Evaluate third-party Responsible AI tools and frameworks.
- Architect and solution AI project lifecycles, from conception to deployment and maintenance.
- Travel may be required depending on business needs and client requirements.
Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.
- Manager experience: 6-8 years; Senior Manager: 8-10 years.
- Architectural experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and their AI services.
- Proficiency in AI and machine learning algorithms and frameworks (e.g., TensorFlow, PyTorch).
- Strong programming skills in languages such as Python, R, or Java.
- Experience with big data technologies (e.g., Hadoop, Spark) and database management systems.
- Knowledge of ethical AI principles, data privacy laws, and regulatory requirements.
- Proven track record of leading and delivering complex AI projects.
- Strong leadership and team management skills, along with excellent problem-solving and analytical abilities.
- Commitment to ethical AI practices and continuous learning.
Bonus Points
- Additional courses or training in responsible AI or ethics in technology.
- Certifications in AI, machine learning, or related fields.
- Experience in specific domains such as healthcare, finance, or retail.
- Familiarity with AI model explainability and interpretability tools.
- Understanding of MLOps, LLMOps, and DevOps practices including CI/CD pipelines for AI systems.
- Containerization capabilities such as Docker and Kubernetes.
- Proficiency in a language other than English at a business level.
