Lead AI Platform Architect for Learning

Pearson

Saint Paul (MN)

On-site

USD 150,000 - 190,000

Full time

6 days ago
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Job summary

Pearson is seeking an experienced AI/ML engineer to lead the design, development, deployment, and operation of production AI capabilities spanning learner intelligence, personalization, recommendations, and knowledge graphs. The role focuses on building reusable platform components, APIs, and workflows to speed AI adoption across Pearson products.

You will own the full AI delivery lifecycle, from prototyping to monitoring and continuous improvement.

Qualifications

  • Bachelor's degree in Computer Science, Engineering, Data Science, AI/ML, or equivalent practical experience.
  • Strong collaboration and communication skills with ability to work with cross-functional teams.

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 with measurable product impact.
  • Build reusable AI platform components, services, APIs, and workflows to accelerate AI adoption across Pearson products.
  • Own the end-to-end AI delivery lifecycle from experimentation to deployment, monitoring, and continuous improvement.
  • Establish scalable MLOps and AIOps practices for model training, deployment, observability, governance, reliability, and cost.
  • Mentor engineers and data scientists to elevate AI engineering capabilities across the organization.

Job description

Pearson is seeking an experienced AI/ML engineer to lead the design, development, deployment, and operation of production AI capabilities spanning learner intelligence, personalization, recommendations, and knowledge graphs. The role focuses on building reusable platform components, APIs, and workflows to speed AI adoption across Pearson products.

You will own the full AI delivery lifecycle, from prototyping to monitoring and continuous improvement.

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