Lead Specialist, AI Scientist at Pearson

Job description

Lead Specialist, AI Scientist Location: Remote, United States About the Role We are seeking a strategic and hands-on Lead Specialist- AI Scientist to design, build, deploy, and scale production AI/ML capabilities that power Pearson's learner intelligence, knowledge graphs, recommendations, personalized learning experiences, and next-generation AI products. This role bridges AI research, data science, software engineering, and product delivery. The successful candidate will lead the development of machine learning, Generative AI, LLM, agentic AI, and knowledge graph solutions, taking them from concept and experimentation through production deployment and continuous improvement. The ideal candidate combines deep technical expertise with a strong execution mindset and a passion for delivering measurable learner and business outcomes. 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. Expected Results: • 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. Qualifications • 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, LangGraph, LangChain, 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. Preferred Qualifications • 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. Apply now and help shape the future of learning. 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 California, Colorado, Hawaii, Illinois, Maryland, Minnesota, New Jersey, New York State, New York City, Vermont, Washington State, and Washington DC laws, the pay range for this position is as follows: The full-time salary range is between $150,000 - 190,000. This position is eligible to participate in an annual incentive program, and information on benefits offered is here . Applications will be accepted through August 30th. This window may be extended depending on business needs.

Requirements

Must-haves

Lead Specialist, AI Scientist
Location: Remote, United States
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.

Nice-to-haves

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.
Apply now and help shape the future of learning.
Applications will be accepted through August 30th. This window may be extended depending on business needs.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningllmpythonci/cdawsazure

Platform: oracle-cloud
Effort: medium