Senior AI Scientist - Learner Experience & Personalization

Pearson

Columbus (OH)

On-site

USD 150,000 - 190,000

Full time

14 days+

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Job summary

Pearson is seeking an AI leader to design, develop, and operate production AI capabilities powering personalized learning experiences. You will deliver Generative AI, LLM, and RAG solutions, build reusable platform components, and own the end-to-end AI delivery lifecycle.

You will establish MLOps/AIOps practices, collaborate with product, engineering, and data teams, and guide governance and responsible AI efforts. Prior experience with cloud-native AI in AWS preferred.

Qualifications

5+ years building and deploying production AI/ML systems.
Experience with cloud-native AI/ML applications and MLOps practices.
Proficiency in Python and modern software engineering practices (APIs, testing, CI/CD, version control).
Experience designing scalable AI platforms and deployment architectures in AWS or similar clouds.
Containerization, orchestration, and infrastructure-as-code in production environments.
Experience evaluating, monitoring, and optimizing AI systems for quality, reliability, latency, and cost.
Familiarity with OpenAI, Anthropic, Bedrock, Azure OpenAI, LangGraph, LangChain, Semantic Kernel, vector databases.
Strong collaboration and communication with product, engineering, and business stakeholders.
Bachelor's degree in CS/Engineering/Data Science/AI or equivalent practical experience.

Responsibilities

Lead the design, development, deployment, and operation of production AI capabilities across Pearson products.
Design and deliver Generative AI, LLM, RAG, and agentic AI solutions with measurable business impact.
Build reusable AI platform components, services, APIs, and workflows for rapid AI adoption.
Own end-to-end AI delivery lifecycle from prototyping to deployment, monitoring, and continuous improvement.
Establish scalable MLOps and AIOps practices for training, deployment, observability, governance, and reliability.
Collaborate with Product, Engineering, Design, Learning Science, and Data Science teams to identify opportunities.
Evaluate emerging AI technologies and architectures balancing quality, safety, scalability, latency, and cost.
Establish best practices for responsible AI, model evaluation, prompt engineering, and governance.
Mentor engineers and data scientists to elevate AI engineering capabilities across the org.
Communicate technical strategy, architecture decisions, trade-offs, risks, and outcomes to stakeholders across the business.