Lead AI Architect for Learner Intelligence & Personalization

Pearson; Tallahassee (FL); On-site; USD 150,000 - 190,000; Full time

Job summary

Pearson seeks a Lead Specialist- AI Scientist to design, build, deploy, and scale production AI/ML capabilities powering learner intelligence, recommendations, and personalized learning experiences.

The role bridges AI research, data science, software engineering, and product delivery, leading GenAI, LLM, RAG, and knowledge graph solutions from concept to production with an execution mindset.

Qualifications

• 5+ years of experience deploying production AI/ML systems.
• Experience with Generative AI, LLMs, RAG, knowledge graphs, or agentic AI.
• Proficient in Python, APIs, testing, CI/CD, and production operations.
• Experience designing cloud-native AI platforms and scalable deployment architectures.

Responsibilities

• Lead design, development, deployment, and operation of production AI capabilities powering Pearson products.
• Design and deliver Generative AI, LLM, RAG, and agentic AI solutions with measurable impact.
• Build reusable AI platform services, APIs, and workflows across Pearson products.
• Own end-to-end AI delivery from experimentation to monitoring and improvement.
• Establish scalable MLOps and AIOps for training, deployment, observability, and governance.
• Collaborate with Product, Engineering, Design, Learning Science, and Data Science teams.
• Evaluate new AI technologies balancing quality, latency, cost, and safety.
• Mentor engineers and data scientists to elevate AI capabilities.

Skills

Python

APIs

CI/CD

MLOps

Production operations

Collaboration

Cloud engineering

Education

Bachelor's degree in Computer Science, Engineering, Data Science, AI/ML

Tools

AWS

Kubernetes

Docker

Terraform

LangChain

LangGraph

Semantic Kernel

Vector databases

OpenAI

Azure OpenAI

Bedrock

Anthropic

Job description

Pearson seeks a Lead Specialist- AI Scientist to design, build, deploy, and scale production AI/ML capabilities powering learner intelligence, recommendations, and personalized learning experiences.

The role bridges AI research, data science, software engineering, and product delivery, leading GenAI, LLM, RAG, and knowledge graph solutions from concept to production with an execution mindset.