Lead AI Scientist: Production ML & Knowledge Graphs

Pearson

Honolulu (HI)

On-site

USD 150,000 - 190,000

Full time

12 days ago

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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, recommendations, and personalized learning experiences.

This role spans AI research, data science, software engineering, and product delivery, leading end-to-end AI delivery lifecycle, MLOps, governance, and collaboration with product and engineering teams to deliver measurable learner and business outcomes.

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.

Responsibilities

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.