AI Architect (NLP, Recommender Systems)

TL;DR

AI Architect (NLP, Recommender Systems): Designing and delivering production-ready AI solutions across enterprise product lines with an accent on end-to-end ML pipelines, scalable cloud infrastructure, and governance-ready deployment. Focus on building NLP and recommender systems, leading a small team of AI Engineers, and ensuring reliable monitoring for performance drift and data integrity.

Location: US Nationwide - Remote

Company

hirify.global builds education technology solutions and supports K-12 schools and school services.

What you will do

• Conceive, prototype, and deploy machine learning models, especially for NLP and recommender systems, across multiple product lines.
• Build and maintain production-ready AI systems using AWS/Azure, containerization, and infrastructure-as-code (including Terraform).
• Own the full lifecycle from data ingestion and model training to deployment, monitoring for performance drift, and iterative improvement.
• Provide direct oversight of AI solution design, infrastructure, and deployment using standardized coding best practices.
• Supervise 1–3 AI Engineers and occasionally flex with UX/front-end and subject matter experts to deliver AI initiatives aligned to business objectives.
• Ensure AI deliverables are documented, reproducible, and monitored for performance and data integrity while supporting governance, compliance, and student data privacy.

Requirements

• 6+ years of related experience (or equivalent combination of education and experience).
• Experience designing and deploying ML models (especially NLP and recommender systems) using Python and common ML libraries.
• Proficiency with AWS, Azure, Docker, Kubernetes, and Terraform to build scalable, secure, high-performing environments.
• Strong analytical skills, attention to detail, and ability to manage multiple projects under critical deadlines.
• Excellent verbal and written communication skills; ability to work independently and in a team environment.
• Ability to complete a required background check.

Nice to have

• Experience with MLOps frameworks such as MLflow or Kubeflow.
• Familiarity with K-12 education data or prior EdTech experience.
• Experience working with responsible/ethical AI and complex governance practices.

Culture & Benefits

• Home-based role with remote work.
• Mentorship and leadership of a small AI engineering team.
• Emphasis on governance, compliance, and responsible AI practices, including student data privacy.
• Focus on cross-functional collaboration with Product, Engineering, IT, and governance/legal stakeholders.

Hiring process

• Interviews to assess applied AI architecture experience, ML pipeline ownership, and leadership/mentoring capability.
• Evaluation of technical depth across NLP/recommenders, cloud infrastructure, and production deployment/monitoring practices.
• Background check as part of the hiring process.