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

Edtech.com's Summary

Pearson is hiring a Senior Software Engineer - Backend/Platform Engineering – AI-First (Agentic + GenAI + RAG). The role involves building and maintaining scalable, reliable AI-enabled backend services and APIs aligned with enterprise AI applications and generative content platforms across various cloud environments. Candidates will work on integrating AI/LLM technologies into production systems, focusing on backend engineering combined with applied AI solutions.

Highlights

Build and maintain backend services and REST APIs supporting AI functionality.
Implement Retrieval-Augmented Generation (RAG) pipelines and integrate enterprise data sources.
Design systems for reliability, performance, cost efficiency, and observability.
Apply strong engineering practices including testing, CI/CD, security-by-design, and documentation.
Use AI-assisted tools daily, such as coding assistants and AI-driven debugging/testing.
Hands-on experience with AI/LLM integration in production or AI output systems required.
Strong experience in building distributed backend services and APIs is mandatory.
Comfortable working with cloud platforms like Azure and/or AWS.
Technology stacks vary: Azure lane (Azure OpenAI, AI Search, MS Teams/Power Platform), PAICE/PRIZM lanes (Java 11+, Spring Boot, AWS), and AWS lane (Java/Spring Boot, Python, microservices, NoSQL).
Focus on enterprise AI solutions such as copilots, generative AI content platforms, content discovery, distribution, and analytics.

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

Pearson is building the next generation of AIâenabled platforms, from enterprise copilots and assistants to AIâpowered 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 multiâtenant APIs that serve many internal consumers.

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

Build distributed services that power AIâenhanced 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 highâperformance backend services in Java/Spring Boot + Python on AWS, integrating LLMs, RAG concepts, and modern AIâassisted 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, AIâassisted 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.

Mustâhave (all roles)

Strong experience building distributed backend services and APIs
Handsâon experience with AI/LLM integration in production or building systems that consume/produce AI outputs
Understanding of prompting/output validation and practical AI tradeâoffs (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 AIâFirst 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.

Pearson is an Equal Opportunity Employer and a member of E-Verify. Employment decisions are based on qualifications, merit and business need. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, sexual orientation, gender identity, gender expression, age, national origin, protected veteran status, disability status or any other group protected by law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.

If you are an individual with a disability and are unable or limited in your ability to use or access our career site as a result of your disability, you may request reasonable accommodations by emailing TalentExperienceGlobalTeam@grp.pearson.com.

Job:

Engineering

Job Family: TECHNOLOGY

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Original Job Description