Senior Software Engineer - AI

Senior Software Engineer – Applied AI & Generative Systems Pearson Learning Studio (PLS) Role Overview

Pearson is accelerating the adoption of applied AI and generative technologies to power next-generation learning, assessment, and knowledge-driven experiences at global scale.

We are seeking a Staff AI Engineer to lead the design, standardization, and delivery of production-grade AI systems that are scalable, reusable, and enterprise-ready.

This is a senior individual contributor role with organization-wide impact. You will define architectural direction, establish engineering standards, and solve complex cross-domain challenges—enabling multiple teams to build high-quality, safe, and performant AI-powered products.

You will operate at the intersection of platform engineering, applied AI, and product innovation, turning cutting‑edge capabilities into reliable, repeatable systems.

• Define and evolve the reference architecture for applied AI and GenAI systems across the organization.
• Establish reusable patterns, frameworks, and abstractions that accelerate development across teams.
• Lead complex design decisions across scalability, latency, cost efficiency, and model performance.
• Drive technical alignment through design reviews, RFCs, and architectural governance.
• Serve as a technical north star for AI system design and engineering rigor.

• Architect and build LLM‑powered systems including:
  • Retrieval‑Augmented Generation (RAG) pipelines
  • Multi‑step reasoning workflows
  • Agentic systems and intelligent assistants
• Design end‑to‑end AI pipelines spanning data ingestion & transformation, embeddings & indexing, inference orchestration, evaluation & feedback loops.
• Move AI solutions from prototype to production scale, ensuring robustness and maintainability.
• Optimize systems for latency, cost, and output quality at scale.

• Build shared AI capabilities and internal platforms consumed by multiple product teams.
• Standardize tooling for prompt/version management, evaluation frameworks, experimentation and A/B testing.
• Enable teams to safely and efficiently integrate AI without reinventing core infrastructure.

• Design systems that enable AI to reason over large‑scale structured and unstructured content.
• Drive architecture for content ingestion pipelines, semantic enrichment and chunking strategies, hybrid search (vector + keyword + metadata).
• Ensure outputs are contextually accurate, explainable, and aligned with domain knowledge.

• Embed responsible AI principles into system design (bias mitigation, guardrails, explainability).
• Ensure compliance with enterprise standards for security, privacy, and governance.
• Design for observability and resilience, covering model performance monitoring, drift detection, failure handling and fallback strategies.
• Proactively identify and mitigate risks related to hallucination, misuse, and data integrity.

• Act as a multiplier for engineering teams, unblocking complex technical challenges.
• Mentor engineers on applied AI best practices, system design, and production readiness.
• Partner with Product, Data Science, and Engineering leaders to turn ambiguous problems into scalable solutions.
• Raise the engineering bar through clear documentation, code quality standards, and design excellence.

• 8–12+ years of software engineering experience, with deep hands‑on work in applied AI / GenAI systems.
• Proven track record of building and operating production‑grade AI systems at scale.
• Strong proficiency in Python and modern distributed/service‑oriented architectures.
• Deep expertise in Large Language Models (LLMs), Retrieval techniques (RAG, hybrid search), Embeddings and vector databases, Prompting strategies and evaluation methods.
• Experience deploying and operating systems in cloud environments (AWS, Azure, or GCP).
• Strong system design skills with cross‑team technical influence.

• Experience building internal AI platforms or shared services used across multiple teams.
• Famil…