Lead Specialist, AI Scientist

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. The posting includes: About the Role (we have that). Responsibilities: "What You'll Do" bullet points. Requirements: maybe "Qualifications" and "Preferred Qualifications". Skills: maybe not explicit separate; but we can treat Qualifications as skills? The instruction: Use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide which sections have content. We have: - About the Role: paragraph after "About the Role". We'll put that in

About the role

... (the paragraph). The paragraph: "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." - Responsibilities: "What You'll Do" bullet list. We'll create

Responsibilities

• ... each bullet. List items from "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: maybe could be part of Responsibilities or separate? Not a required heading. Could be included under Responsibilities as additional bullet? But we can keep as part of Responsibilities or maybe separate "Expected Results" not in list of allowed headings; we should not create extra headings. So we could incorporate Expected Results as part of Responsibilities or maybe as a separate paragraph under Responsibilities. Safer to include as part of Responsibilities as additional bullet? But they are results, not duties. Could add as a paragraph after the list under Responsibilities. That's fine. - Qualifications: The "Qualifications" section includes bullet list of required qualifications. We'll put under

  Qualifications

  ... each bullet. List: - 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: Could be under "Qualifications" as separate list? Or we could create a "Preferred Qualifications" heading not in allowed list; but we could incorporate into Qualifications as a sublist? The instruction: Use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Preferred Qualifications could be considered part of Qualifications. We'll include them as a sub-list under Qualifications maybe with a note "Preferred". But we must not invent headings not allowed. So we can include them as part of Qualifications, maybe separate
  with a preceding

  Preferred qualifications:

  then list. - Skills: Not explicitly listed separate; but we could derive from qualifications. However we should not create a Skills section if no specific content beyond what's already in Qualifications. The instruction: Prefer a few substantive sections over many thin ones. So we might omit Skills if not distinct. - Benefits: The posting mentions: "This position is eligible to participate in an annual incentive program, and information on benefits offered is here." That's a link but no detail. Not specific benefits listed. So we should not create Benefits section because no specific content. - Pay: The posting includes compensation range: "The full-time salary range is between $150,000 - 190,000." Also mention annual incentive program. So we have Pay content. We'll create

  Pay

  The full-time salary range is between $150,000 - $190,000. This position is eligible to participate in an annual incentive program.

  - Schedule: The posting includes metadata: "Schedule: FULL_TIME". Also "Workplace Type: Hybrid". So we have schedule info. We'll create

  Schedule

  Full-time, Hybrid workplace.

  - Location: "Location: Remote, United States". Could be included maybe in About the role or separate? Not a heading; we could include location in About the role paragraph or as a short p. But we already have About the role paragraph; we could add location after that. However we must not repeat job title. We can add a

  Location: Remote, United States.

  maybe under About the role or separate? The instruction: Do not put Job ID / requisition numbers as headings — fold minor metadata into a short

  or drop it. So we can include location as a short p after About the role. Also there is "Job: Data Engineering Job Family: TECHNOLOGY Organization: Higher Education Schedule: FULL_TIME Workplace Type: Hybrid Req ID: 24600 #location". That's metadata; we can drop or fold into short p. We already captured schedule and workplace type. We could drop the rest. Now produce HTML fragment:

  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.

  Location: Remote, United States.

  Responsibilities

  • 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.