Senior Manager, Responsible AI Solutions

Responsibilities

• Develop and implement ethical AI solutions: Collaborate with cross-functional teams to identify and translate business challenges into AI solutions. This involves assessing organizational needs, recommending ethical solutions, gathering requirements, designing and developing AI solutions, and deploying them into production environments.
• Manage and oversee AI program: Includes overall program and actions to plan, execute, and deliver AI projects, ensuring they are completed on time, within budget, and meet the required quality standards. This includes defining project scope, gaining insight and agreement from others, allocating resources, managing timelines, and mitigating risks.
• Define and track key performance indicators (KPIs): Establish and monitor KPIs to measure the success and impact of AI initiatives, ensuring alignment with business objectives and providing data-driven insights for continuous improvement.
• Evaluate and optimize AI model performance: Continuously monitor and evaluate the effectiveness and performance of AI models, identifying areas for improvement and implementing optimization strategies to reduce our carbon footprint and enhance accuracy, efficiency, and scalability.
• Ensure compliance with ethical AI practices: Adhere to ethical guidelines and data privacy regulations in all AI-related activities. Promote responsible AI development and deployment within the organization, considering potential biases, fairness, and transparency in AI systems.
• Monitor AI advancements: Remain up-to-date with emerging AI trends, technologies, and best practices. Research and recommend new AI tools and techniques to improve operational efficiency and drive innovation.
• Communicate effectively with stakeholders: Clearly and effectively communicate project solutions, requirements and updates, technical details, and AI concepts to technical and non-technical audiences, including senior management, business stakeholders, and team members. Translate complex technical information into clear business value for non-technical stakeholders.

Skillset

• Proficient in Python and AI frameworks (e.g., TensorFlow, PyTorch), with expertise in machine learning, deep learning, and natural language processing, including Gen AI technologies.
• Experienced with Generative AI tools and techniques (e.g., LLMs, LangChain, retrieval-augmented generation) and Agentic architecture.
• Familiar with AI Ops principles, including CI/CD pipelines, containerization, and automated testing.
• Skilled in optimizing AI workflows (e.g., distributed training, inference optimization, resource utilization – GPU/TPU) through collaboration with AI experts.
• Experienced in deploying AI models to production cloud environments (AWS, Azure, GCP), considering performance, cost, and latency.
• Adept at monitoring and troubleshooting model performance, addressing issues like performance drift and latency.
• Familiar with cloud computing platforms (AWS, Azure, GCP) and data technologies (SQL, Hadoop, Spark) for AI development and deployment.
• Demonstrated ability to recommend and apply AI solutions to real-world problems in content-rich domains.
• Experienced in working with large datasets and extracting insights from unstructured data.
• Strong understanding of content management systems, knowledge graphs, information retrieval techniques, and experience with agile development methodologies.
• Excellent project management skills, including experience managing complex AI projects.
• Ways of working and engaging that align with the Foundation’s mission, core values, and commitment to creating a culture of excellence.