Job Description

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 (these are production roles)
• Implement strong engineering practices: testing, CI/CD, security-by-design, documentation
• Use AI in your daily workflow (agentic coding assistants, AI‑assisted debugging/testing) these roles expect it as standard practice

What we’re looking for

We don’t expect everyone to match everything, but we do need strong engineering fundamentals plus real AI exposure.

Must‑have (all roles)

• Strong experience building distributed backend services and APIs
• Hands‑on experience with AI/LLM integration in production or building systems that consume/produce AI outputs
• Understanding of prompting/output validation and practical AI trade‑offs (latency, cost, quality)
• Comfort working in cloud environments (Azure and/or AWS)

Likely stack alignment (varies by lane)

• Azure lane: Azure OpenAI / Azure AI services, RAG with Azure AI Search, Teams/Power Platform integrations
• PAICE/PRIZM lanes: Java 11+ and Spring Boot; strong API discipline; platform services at scale. PAICE is described internally as a suite of enterprise services hosted in AWS for content creation and delivery.

• AWS lane: Java/Spring Boot + Python; AWS services; microservices; NoSQL exposure (where required)

This advert covers multiple approved roles across our AI‑First Engineering teams. If there’s a strong match with our vacancies, a member of our Talent Acquisition team will reach out to start a conversation and confirm your preferences.