Lead Specialist, AI Scientist

What You ll Do

• Lead the design, development, deployment, and operation of production AI capabilities supporting learner intelligence, personalization, recommendations, knowledge graphs, and AI-powered learning experiences.
• Design and deliver Generative AI, LLM, retrieval-augmented generation (RAG), and agentic AI solutions that create measurable product and business impact.
• Build reusable AI platform capabilities, services, APIs, and workflows that accelerate AI adoption across Pearson products.
• Own the end-to-end AI delivery lifecycle, from experimentation and prototyping through deployment, monitoring, evaluation, and continuous improvement.
• Establish scalable MLOps and AIOps practices for model training, deployment, observability, governance, reliability, and operational excellence.
• Partner closely with Product, Engineering, Design, Learning Science, and Data Science teams to identify opportunities and deliver impactful AI-powered capabilities.
• Evaluate emerging AI technologies, foundation models, and architectural approaches while balancing quality, safety, scalability, latency, and cost.
• Establish best practices for responsible AI, model evaluation, prompt engineering, agent evaluation, and AI governance.
• Mentor engineers and data scientists and help elevate AI engineering capabilities across the organization.
• Communicate technical strategy, architecture decisions, trade-offs, risks, and outcomes to stakeholders across the business.

• Production-ready learner intelligence, recommendation, and knowledge graph capabilities powering personalized learning experiences.
• Enterprise-scale AI services, LLM applications, and agentic workflows integrated into Pearson products.
• Reusable AI platform components enabling rapid development, evaluation, deployment, and scaling of AI-powered capabilities.
• Reliable, secure, observable, and cost-efficient AI systems operating successfully in production environments.
• Accelerated transition of AI prototypes and research into measurable product and business outcomes.
• Improved learner engagement, efficacy, productivity, and business impact through deployed AI capabilities.

• 5+ years of experience building and deploying production AI/ML systems, including cloud-native applications and MLOps practices.
• Strong experience with applied machine learning, Generative AI, LLMs, RAG architectures, recommendation systems, knowledge graphs, or agentic AI solutions.
• Hands‑on experience building and deploying AI applications using foundation models and modern AI frameworks.
• Proficiency in Python and modern software engineering practices, including APIs, testing, CI/CD, version control, and production operations.
• Experience designing scalable AI platforms, services, and deployment architectures in AWS or similar cloud environments.
• Experience with containerization, orchestration, infrastructure-as-code, and production‑grade deployment practices.
• Experience evaluating, monitoring, and optimizing AI systems for quality, reliability, safety, latency, scalability, and cost.
• Experience with modern AI technologies such as OpenAI, Anthropic, Bedrock, Azure OpenAI, Lang Graph, Lang Chain, Semantic Kernel, vector databases, or similar platforms.
• Strong collaboration and communication skills with product, engineering, and business stakeholders.
• Bachelor s degree in Computer Science, Engineering, Data Science, AI/ML, or equivalent practical experience.

• Master s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related discipline.
• Experience in educational technology, personalized learning, assessment, learning science, or related domains.
• Familiarity with psychometrics, proficiency modeling, Bayesian methods, item response theory, or educational measurement.
• Experience building AI platforms, knowledge graph solutions, or agentic systems at enterprise scale.
• Contributions to research, patents, open‑source projects, or industry thought leadership.

Compensation at Pearson is influenced by a wide array of factors including but not limited to skill set, level of experience, and specific location. As required by the…