Lead Specialist, AI Scientist

Pearson; Jackson (MS); On-site; USD 150,000 - 190,000; Full time

Job summary

Pearson is seeking a seasoned AI/ML lead to design, deploy, and operate production AI capabilities that power learner intelligence, personalization, and knowledge graphs across Pearson products.

You will build Generative AI, LLM, and RAG solutions, establish scalable MLOps/AIOps, and mentor engineers. Collaboration with Product, Engineering, and Learning Science is essential for impactful outcomes.

Qualifications

• 5+ years building and deploying production AI/ML systems in cloud environments.
• Hands-on with Generative AI, LLMs, RAG, and agentic AI concepts.
• Proficient in Python and modern software practices (APIs, testing, CI/CD, version control).
• Experience designing scalable AI platforms and deployment architectures on AWS.
• Familiar with containerization, orchestration, and IaC for production systems.

Responsibilities

• Lead design, development, deployment, and operation of production AI capabilities for learner intelligence, personalization, and recommendations.
• Design and deliver AI/LLM/RAG solutions with measurable product impact.
• Build reusable AI platform components, APIs, and workflows for Pearson products.
• Own end-to-end AI delivery lifecycle from experiments to production; monitor and improve.
• Establish scalable MLOps and AIOps practices for training, deployment, observability, and governance.
• Collaborate with Product, Engineering, Design, Learning Science, and Data Science teams.
• Evaluate emerging AI tech, foundation models, and architectural approaches balancing quality, latency, and cost.
• Promote responsible AI practices, model evaluation, prompt engineering, and governance.

Skills

ML engineering

Generative AI

LLMs/RAG

Python

APIs/CI-CD

Cloud AWS

MLOps

Knowledge graphs

Collaboration

Bachelor's degree (CS/DS/AI)

Education

Bachelor's degree in Computer Science, Engineering, Data Science, AI/ML, or equivalent practical experience

Master's degree or PhD in related field

Tools

AWS

Kubernetes

CI/CD pipelines

LangChain

Semantic Kernel

Vector databases

LangGraph

OpenAI API

Azure OpenAI

Job description

What You\'ll Do

• Lead the design, development, deployment, and operation of production AI capabilities supporting learner intelligence, personalization, recommendations, knowledge graphs, and AI-powered learning experiences.


• Design and deliver Generative AI, LLM, retrieval-augmented generation (RAG), and agentic AI solutions that create measurable product and business impact.


• Build reusable AI platform capabilities, services, APIs, and workflows that accelerate AI adoption across Pearson products.


• Own the end-to-end AI delivery lifecycle, from experimentation and prototyping through deployment, monitoring, evaluation, and continuous improvement.


• Establish scalable MLOps and AIOps practices for model training, deployment, observability, governance, reliability, and operational excellence.


• Partner closely with Product, Engineering, Design, Learning Science, and Data Science teams to identify opportunities and deliver impactful AI-powered capabilities.


• Evaluate emerging AI technologies, foundation models, and architectural approaches while balancing quality, safety, scalability, latency, and cost.


• Establish best practices for responsible AI, model evaluation, prompt engineering, agent evaluation, and AI governance.


• Mentor engineers and data scientists and help elevate AI engineering capabilities across the organization.


• Communicate technical strategy, architecture decisions, trade-offs, risks, and outcomes to stakeholders across the business.

Expected Results:

• Production-ready learner intelligence, recommendation, and knowledge graph capabilities powering personalized learning experiences.


• Enterprise-scale AI services, LLM applications, and agentic workflows integrated into Pearson products.


• Reusable AI platform components enabling rapid development, evaluation, deployment, and scaling of AI-powered capabilities.


• Reliable, secure, observable, and cost-efficient AI systems operating successfully in production environments.


• Accelerated transition of AI prototypes and research into measurable product and business outcomes.


• Improved learner engagement, efficacy, productivity, and business impact through deployed AI capabilities.

Qualifications

• 5+ years of experience building and deploying production AI/ML systems, including cloud-native applications and MLOps practices.


• Strong experience with applied machine learning, Generative AI, LLMs, RAG architectures, recommendation systems, knowledge graphs, or agentic AI solutions.


• Hands‑on experience building and deploying AI applications using foundation models and modern AI frameworks.


• Proficiency in Python and modern software engineering practices, including APIs, testing, CI/CD, version control, and production operations.


• Experience designing scalable AI platforms, services, and deployment architectures in AWS or similar cloud environments.


• Experience with containerization, orchestration, infrastructure-as-code, and production‑grade deployment practices.


• Experience evaluating, monitoring, and optimizing AI systems for quality, reliability, safety, latency, scalability, and cost.


• Experience with modern AI technologies such as OpenAI, Anthropic, Bedrock, Azure OpenAI, LangGraph, LangChain, Semantic Kernel, vector databases, or similar platforms.


• Strong collaboration and communication skills with product, engineering, and business stakeholders.


• Bachelor\'s degree in Computer Science, Engineering, Data Science, AI/ML, or equivalent practical experience.

Preferred Qualifications

• Master\'s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related discipline.


• Experience in educational technology, personalized learning, assessment, learning science, or related domains.


• Familiarity with psychometrics, proficiency modeling, Bayesian methods, item response theory, or educational measurement.


• Experience building AI platforms, knowledge graph solutions, or agentic systems at enterprise scale.


• Contributions to research, patents, open‑source projects, or industry thought leadership.

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 minimum full-time salary range is between $150,000 - 190,000.

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

Applications will be accepted through August 30th. This window may be extended depending on business needs.

Who we are:

At Pearson, our purpose is simple: to help people realize the life they imagine through learning. We believe that every learning opportunity is a chance for a personal breakthrough. We are the world\'s lifelong learning company. For us, learning isn't just what we do. It\'s who we are. To learn more: We are Pearson.

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 [e-mail].