Senior Software Engineer - Backend/Platform Engineering – AI‑First (Agentic + GenAI + RAG)

About this role

Pearson is building the next generation of AI‑enabled platforms, from enterprise copilots and assistants to AI‑powered content creation, discovery, and analytics. We’re looking for engineers who love shipping production systems and want to work at the intersection of backend engineering + applied AI.

This advert covers multiple approved roles across Pearson. The exact team you join may vary, but the work is aligned to one theme: building reliable, scalable AI systems that real teams and products depend on.

The kind of problems you’ll solve

Depending on the role, you may work on one (or more) of these areas:

1) Enterprise Agentic Solutions (Microsoft stack)

Build copilots, assistants, and intelligent workflows using Azure OpenAI/Azure AI, including RAG with Azure AI Search and integrations into Microsoft tools (Teams/Power Platform).

2) PAICE – Content Gen AI Platform (GenAI “engine”)

Build and operate the services behind Pearson’s generative AI ecosystem, orchestration across foundation models, guardrails, analytics, and multi‑tenant APIs that serve many internal consumers.

3) PAICE – PRIZM (Discovery, distribution & analytics)

Build distributed services that power AI‑enhanced content discovery, delivery, and analytics at scale, with strong engineering fundamentals, production ownership, and mentoring. PRIZM is positioned internally as a content discovery/distribution/analytics ecosystem within PAICE.

4) Cloud AI Backend Engineering (AWS + Java/Python)

Build high‑performance backend services in Java/Spring Boot + Python on AWS, integrating LLMs, RAG concepts, and modern AI‑assisted development tools while delivering scalable APIs and microservices.

What you’ll do (core themes across all roles)

- Build and maintain backend services and REST APIs that support AI functionality
- Implement RAG pipelines and integrate with enterprise data sources where required
- Design for reliability, performance, cost, and observability (the...