AI Architect – Enterprise ML & NLP Leader (Remote)

Stride; United States; Remote; USD 113,000 - 200,000; Full time

Job summary

Stride, Inc. is seeking an AI Architect to design and deliver production-ready AI solutions across the organization.

This role leads a small team of AI Engineers and coordinates with UX, front-end, and subject matter experts to build end-to-end ML pipelines in cloud environments and align with business objectives in the K12 context. You will oversee solution design, infrastructure, deployment, governance, and responsible AI practices, mentoring engineers and ensuring documentation, monitoring,

Qualifications

• Bachelor's degree in Computer Science, Math, Physics, Engineering, or equivalent.
• Six (6) years' related experience; OR
• Equivalent combination of education and experience

Responsibilities

• Drives conception, prototyping, and deployment of ML models across product lines.
• Leverages AWS and Azure ecosystems, containers, and IaC for production-ready AI systems.
• Oversees data ingestion, model training, deployment, monitoring, and iteration for improvement.
• Directly supervises 1-3 AI Engineers and flex contributors.
• Collaborates with Product, Engineering, IT Managers, Data Stewards, and executives to align AI initiatives with objectives.
• Ensures AI deliverables are documented, reproducible, and monitored for performance and data integrity.

Skills

ML model design

NLP expertise

Python

AWS/Azure

Docker/Kubernetes

Terraform

Team leadership

Governance & privacy

Strong communication

Education

Bachelor's degree

Tools

AWS

Azure

Docker

Kubernetes

Terraform

ML libraries

Job description

Stride, Inc. is seeking an AI Architect to design and deliver production-ready AI solutions across the organization.

This role leads a small team of AI Engineers and coordinates with UX, front-end, and subject matter experts to build end-to-end ML pipelines in cloud environments and align with business objectives in the K12 context. You will oversee solution design, infrastructure, deployment, governance, and responsible AI practices, mentoring engineers and ensuring documentation, monitoring,