Remote AI Scientist Lead GenAI, LLMs & Knowledge Graphs

Pearson

Trenton (NJ)

Hybrid

USD 150,000 - 190,000

Full time

14 days+

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Benefits offered by this job

Annual incentive program

Job summary

Pearson is seeking a strategic and hands-on Lead Specialist- AI Scientist to design, build, deploy, and scale production AI/ML capabilities powering learner intelligence, knowledge graphs, and personalized learning experiences.

The role bridges AI research, data science, software engineering, and product delivery. You will lead ML/LLM, RAG, and agentic AI solutions from concept to production, with an emphasis on measurable learner and business outcomes.

Qualifications

5+ years of experience building production AI/ML systems.
Strong experience with Generative AI, LLMs, RAG architectures, knowledge graphs, or agentic AI solutions.
Hands-on experience with foundation models and modern AI frameworks.
Proficiency in Python and production software practices, including APIs, testing, CI/CD.
Experience designing scalable AI platforms and deployment architectures in AWS or similar cloud environments.

Responsibilities

Lead the design, deployment, and operation of production AI capabilities powering 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 components, 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.