ML Infrastructure Engineer (AI)

TL;DR

ML Infrastructure Engineer (AI): Building the foundation for AI and machine learning capabilities across the product portfolio with an accent on model experimentation, training, and deployment at scale. Focus on designing production-grade inference systems, automating ML lifecycle workflows, and optimizing GPU-based infrastructure.

Location: Must be based in the United States (Remote options available)

Salary: $145,000 - $165,000

Company

hirify.global is an AI-powered influencer marketing platform that helps brands identify creators and execute data-driven campaigns.

What you will do

• Define and own the long-term ML infrastructure roadmap and model lifecycle management standards.
• Design and maintain production-grade deployment and inference systems using CI/CD pipelines, Docker, and Flask.
• Automate end-to-end ML workflows, including training pipelines, model validation, and registry management.
• Operate GPU-based infrastructure and manage workloads across AWS and GCP environments.
• Develop and maintain scalable cloud environments using infrastructure-as-code (Terraform, CloudFormation).
• Implement robust monitoring for model performance, latency, and drift detection using Prometheus and Grafana.

Requirements

• 4+ years of experience in ML Ops, ML infrastructure, or backend engineering for production ML systems.
• Strong experience with Amazon SageMaker, Docker, and Flask-based APIs.
• Hands-on experience with container orchestration platforms like Kubernetes, EKS, or GKE.
• Proficiency in Python; experience with IaC tools like Terraform or CloudFormation.
• Experience managing GPU-based workloads and scaling inference systems.
• Must be based in the United States.

Nice to have

• Experience supporting LLMs or generative AI pipelines.
• Knowledge of distributed training systems or feature stores (e.g., Feast).
• Additional programming experience in Go, Java, or Scala.
• Familiarity with real-time inference systems or ML governance frameworks.

Culture & Benefits

• Market-based and data-driven compensation with bi-annual evaluations.
• Eligibility for various benefits plans for all permanent team members.
• Inclusive culture focused on diversity and "culture add" rather than "culture fit".
• Flexible remote work options for select positions.
• Environment that values automation, reliability, and continuous improvement.