AI Infrastructure Engineer (AI)

TL;DR

AI Infrastructure Engineer (AI): Building scalable, secure, and production-ready infrastructure that powers ML workflows end-to-end with an accent on model lifecycle systems, API and backend development, and AWS infrastructure management. Focus on designing robust, cloud-native systems, optimizing reliability, security, and performance, and contributing to CI/CD and container orchestration for machine learning pipelines.

Location: Remote anywhere

Company

hirify.global is a leading cloud-native engineering firm redefining how modern data and AI systems are built for pioneering technology companies.

What you will do

• Design, implement, and maintain robust infrastructure for ML workflows across real-time and batch environments.
• Build and support production-grade model lifecycle systems, including registration, versioning, and deployment workflows.
• Develop APIs and backend services in TypeScript and Python to support model integration and orchestration.
• Manage and optimize infrastructure using AWS and infrastructure-as-code (CDK preferred).
• Work with Databricks MLFlow for end-to-end model management.
• Monitor and improve infrastructure reliability, security, and performance.

Requirements

• 7+ years in software or infrastructure engineering with proven experience supporting AI/ML systems.
• Deep hands-on experience with AWS services and modern IaC practices (Terraform/CDK).
• Strong backend programming skills in TypeScript and Python.
• Production-level use of MLFlow for model management and deployment.
• Expertise in containerization (Docker), CI/CD automation, and orchestration tools.
• Solid understanding of designing scalable and secure systems in cloud-native environments.
• Strong communication skills, able to bridge gaps between engineering and product stakeholders and comfortable working across time zones.

Nice to have

• Exposure to LLM infrastructure and frameworks (e.g., DSPy, LangChain).
• Knowledge of LLM performance metrics: latency, cost monitoring, and usage optimization.
• Familiarity with semantic search tools and vector stores (e.g., OpenSearch, Pinecone).

Culture & Benefits

• Remote anywhere with flexible working hours.
• Coworking space financial coverage.
• Paid days off annually.
• Workspace program: $2500 for work equipment of your choice.
• Paid courses and certifications (e.g., AWS, CKA, ML).
• Participation at international conferences.
• B2B with multiple benefits.