Lead AI Scientist - Production Platform & Personalization

Pearson; Charleston (WV); On-site; USD 150,000 - 190,000; Full time

Job summary

Pearson in the United States seeks a senior AI/ML leader to design, deploy, and operate production AI capabilities powering learner intelligence, personalization, and knowledge graphs. You will own end-to-end AI delivery, mentor engineers, and partner with product, design, and data science teams to drive measurable business impact.

Responsibilities include building scalable AI platforms, applying Generative AI, LLMs, RAG, and agentic solutions, while establishing MLOps, governance, and

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

Python

APIs & CI/CD

AWS

MLOps

LLMs & Generative AI

RAG architectures

Knowledge graphs

Agentic AI

Foundation models

LangChain

Vector databases

Collaboration

Education

Bachelor's degree in CS/Eng/DS/AI

Tools

Docker

Kubernetes

Terraform

IaC

Job description

Pearson in the United States seeks a senior AI/ML leader to design, deploy, and operate production AI capabilities powering learner intelligence, personalization, and knowledge graphs. You will own end-to-end AI delivery, mentor engineers, and partner with product, design, and data science teams to drive measurable business impact.

Responsibilities include building scalable AI platforms, applying Generative AI, LLMs, RAG, and agentic solutions, while establishing MLOps, governance, and