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 .

Key Responsibilities

Technical Leadership & Architecture

+ 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.

Applied GenAI Systems (Core Focus)

+ 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 production scale , ensuring robustness and maintainability.

+ Optimize systems for latency, cost, and output quality at scale .

AI Platform & Reusability

+ 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.

Content & Knowledge Intelligence

+ 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 .

Reliability, Safety & Responsible AI

+ 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 :

+ Model performance monitoring

+ Drift detection

+ Failure handling and fallback strategies

+ Proactively identify and mitigate risks related to hallucination, misuse, and data integrity .

Influence & Technical Mentorship

+ 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 .

Required Qualifications

+ 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 .

Preferred Qualifications

+ Experience building internal AI platforms or shared services used across multiple teams.

+ Familiarity with agentic architectures and workflow orchestration frameworks .

+

Experience with ML/ LLMOps practices , including:

+ Monitoring and observability

+ Model/version lifecycle management

+ Evaluation pipelines

+ Exposure to education, knowledge systems, personalization, or assessment domains .

+

Experience with high-scale content systems or search platforms .

_This is a hybrid work setup, where the candidate will be required to work three days onsite at our Hoboken office._

_Applications will be accepted through April 27. This window may be extended depending on business needs._

_Compensation at Pearson is influenced by a wide array of factors including but not limited to skill set, level of experience, and specific location. As required by…