Lead Specialist, AI Scientist
New
P
PearsonEducational technology
Remote, United StatesFull-TimeLead
SalaryThe full-time salary range is between $150,000 - 190,000.
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Job Details
- Experience
- 5+ years of experience building and deploying production AI/ML systems, including cloud-native applications and MLOps practices.
- Required Skills
- AWSPythonMachine LearningCI/CDMLOpsGenerative AI
Requirements
- 5+ years of experience building and deploying production AI/ML systems, including cloud-native applications and MLOps practices.
- Experience with applied machine learning, Generative AI, LLMs, RAG architectures, recommendation systems, knowledge graphs, or agentic AI.
- 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 technologies such as OpenAI, Anthropic, Bedrock, Azure OpenAI, LangGraph, LangChain, Semantic Kernel, or vector databases.
- Bachelor's degree in Computer Science, Engineering, Data Science, AI/ML, or equivalent practical experience.
- Preferred: master's degree or PhD in a related discipline, or experience in educational technology, personalized learning, assessment, learning science, or related domains.
- Preferred: familiarity with psychometrics, proficiency modeling, Bayesian methods, item response theory, or educational measurement.
Responsibilities
- Lead the design, development, deployment, and operation of production AI capabilities for 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.
- Build reusable AI platform capabilities, services, APIs, and workflows.
- Own the 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, and reliability.
- Partner with Product, Engineering, Design, Learning Science, and Data Science teams to identify and deliver AI capabilities.
- Evaluate AI technologies, foundation models, and architectures for quality, safety, scalability, latency, and cost.
- Establish practices for responsible AI, model and agent evaluation, prompt engineering, and AI governance.
- Mentor engineers and data scientists and communicate technical strategy, architecture decisions, risks, and outcomes to stakeholders.
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