AI Architect

Position Overview

The AI Architect is responsible for designing and delivering production-ready AI solutions across the organization. This role manages a small team of AI Engineers and flexes roles from UX, front-end, and subject matter experts to create innovative machine learning solutions for enterprise-wide impact. The position provides direct oversight of AI solution design, infrastructure, and deployment using standardized coding best practices; mentors team members in technical development; and ensures alignment with business objectives in the K12 and broader enterprise context.

Key Responsibilities

• Drives the conception, prototyping, and deployment of machine learning models—particularly in natural language processing (NLP) and recommender systems—across multiple product lines.
• Leverages AWS and Azure ecosystems, containerization, and infrastructure-as-code tools to create and maintain production-ready AI systems that can scale efficiently.
• Works from proof-of-concept through full productization, including data ingestion, model training, deployment, monitoring for performance drift, and iteration for continuous improvement.
• Informs, influences, and supports product decisions.
• Directly supervises 1–3 AI Engineers and occasional flex contributors, providing guidance on coding best practices, model development, and data governance.
• Collaborates with Product, Engineering, IT Managers, Data Stewards, and executives to align AI initiatives with organizational objectives and ensure compliance with governance and data privacy requirements.
• Provides critical input to identify practical opportunities for applied AI and drives the timely delivery of AI products that lead to actionable business decisions.
• Ensures AI deliverables are well-documented, reproducible, and monitored for performance and data integrity, while adapting to evolving data privacy regulations and responsible AI practices.
• Keeps abreast of emerging trends and tools—such as large language models—and advocates for robust DevOps/MLOps, agile methodologies, and cross-functional collaboration.
• Aligns K12’s AI capabilities with the needs of schools and school services teams to drive the use of actionable data for improved outcomes.
• Defines strategic opportunities for leveraging data science across businesses and functions in support of K12’s mission, vision, and long-term strategy.
• Demonstrates a passion for education and actively motivates team members to share that passion.

Required Qualifications

• Bachelor's degree in Computer Science, Math, Physics, Engineering, or a related quantitative field – or equivalent combination of education and at least six (6) years of related experience.
• Experience designing and deploying machine learning models—especially NLP and recommender systems—using Python and popular ML libraries (e.g., PyTorch, TensorFlow, scikit-learn).
• Proficiency with AWS, Azure, Docker, Kubernetes, and Terraform to build scalable, secure, and high-performing environments.
• High attention to detail along with strong analytical, planning, and communication skills; ability to manage multiple projects simultaneously; and proficiency with Microsoft Office and modern reporting tools.
• Certifications such as AWS Solutions Architect or Azure Solutions Architect are a plus (not mandatory).

Preferred Qualifications

• Experience with MLOps frameworks such as MLflow or Kubeflow.
• Familiarity with K12 education data or prior EdTech experience.
• Experience in environments emphasizing responsible AI, ethical AI, or complex governance practices.

Benefits & Perks

• Compensation: Salary range from $113,073.75 to $180,000.00 with eligibility for bonuses. Salary levels are determined based on education, experience, qualifications, and other key factors.
• Benefits: Robust benefits package that may include health benefits, retirement contributions, and paid time off.