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 the California, Colorado, Hawaii, Illinois, Maryland, Minnesota, New Jersey, New York State, New York City, Vermont, Washington State, and Washington DC laws, the pay range for this position is as follows:  

The full-time salary range for this role is between $140,000 - $160,000

This position is eligible to participate in an annual incentive program, and information on benefits offered is here