Senior AI Architect: Learner Intelligence & Personalization

Pearson

Dover (DE)

On-site

USD 150,000 - 190,000

Full time

7 days ago

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Job summary

Pearson seeks a senior AI leader to design, build, and operate production AI capabilities across learner intelligence, recommendations, and knowledge graphs. You will drive Generative AI, LLMs, and RAG initiatives, partner with Product, Engineering, Learning Science, and Data Science teams, and establish scalable MLOps/AIOps across Pearson products.

The role requires 5+ years in production AI/ML, strong Python/API/CI/CD skills, and experience with cloud deployments (AWS) in educational tech

Qualifications

5+ years of experience building and deploying production AI/ML systems, including cloud-native applications and MLOps practices.
Experience with 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 design, development, deployment, and operation of production AI capabilities supporting learner intelligence and personalized experiences.
Design and deliver Generative AI, LLM, RAG, and agentic AI solutions with measurable business impact.
Build reusable AI platform capabilities, services, APIs, and workflows for Pearson products.
Own end-to-end AI delivery lifecycle from experimentation to deployment, monitoring, and improvement.
Establish scalable MLOps and AIOps practices for training, deployment, observability, governance, reliability, and cost.
Partner with Product, Engineering, Design, Learning Science, and Data Science to identify opportunities.
Evaluate emerging AI tech and architectures balancing quality, safety, scalability, latency, and cost.
Establish best practices for responsible AI, model evaluation, prompt engineering, and governance.
Mentor engineers and data scientists to elevate AI engineering capabilities.
Communicate strategy, architecture decisions, risks, and outcomes to stakeholders.