Lead AI Scientist: Generative AI & Platform Leader

Pearson; Sacramento (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Pearson is seeking a senior AI/ML leader to shape production AI capabilities that power personalized learning experiences, with emphasis on learner intelligence, recommendations, and knowledge graphs. You will drive end-to-end AI delivery, collaborate with product and engineering, and establish scalable MLOps/AIOps to ensure reliable, cost-efficient solutions.

Responsibilities include architecting Generative AI, RAG, and agentic AI solutions, mentoring engineers, and communicating strategic

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, 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.

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.

Skills

Applied machine learning

Generative AI / LLMs

RAG architectures

Knowledge graphs

Agentic AI

Python

APIs

CI/CD

Education

Bachelor's degree in Computer Science, Engineering, Data Science, AI/ML, or equivalent practical experience

Master's degree or PhD in Computer Science, AI, ML, or related field

Job description

Pearson is seeking a senior AI/ML leader to shape production AI capabilities that power personalized learning experiences, with emphasis on learner intelligence, recommendations, and knowledge graphs. You will drive end-to-end AI delivery, collaborate with product and engineering, and establish scalable MLOps/AIOps to ensure reliable, cost-efficient solutions.

Responsibilities include architecting Generative AI, RAG, and agentic AI solutions, mentoring engineers, and communicating strategic