Senior AI Infrastructure Engineer (AI)

TL;DR

Senior AI Infrastructure Engineer (AI): You will own the infrastructure that brings AI models to life in production, optimizing LLM inference, deploying real-time voice AI agents, and scaling GPU clusters. Focus on inference optimization, real-time video processing, model serving at scale, and GPU workload orchestration.

Location: Europe. Must be based in Portugal or Germany.

Salary: €66,500 - €104,500 a year

Company

hirify.global is shifting healthcare from human-first to AI-first through its AI Care platform, making world-class healthcare available anytime, anywhere.

What you will do

• Design, build, and maintain the inference infrastructure that powers hirify.global's AI products, ensuring high throughput, low latency, and cost efficiency.
• Own the end-to-end deployment pipeline for AI models - from real-time computer vision to large language models.
• Architect and scale Kubernetes clusters for GPU-accelerated workloads, including autoscaling strategies and resource scheduling.
• Build and operate the infrastructure behind hirify.global's real-time AI agents, including WebRTC cluster provisioning.
• Drive inference scaling strategies and evaluate emerging AI infrastructure tools to keep hirify.global at the cutting edge.
• Collaborate with ML Engineers, Data Scientists, and Product teams to translate model requirements into robust, production-ready infrastructure.

Requirements

• 5+ years of experience in infrastructure engineering, with at least 2 years focused on AI/ML workloads in production environments.
• Strong experience with Kubernetes for orchestrating GPU-accelerated workloads.
• Hands-on experience with model serving and inference optimization frameworks for both real-time computer vision and large language model workloads.
• Solid understanding of LLM inference optimization techniques.
• Experience with Infrastructure as Code (Terraform or similar) and GitOps methodologies for managing complex, GPU-enabled environments.
• Fluent in English (written and oral).

Nice to have

• Experience with LLM serving engines such as vLLM, SGLang, or LLM-D.
• Experience with NVIDIA Triton Inference Server and TensorRT for real-time computer vision workloads.
• Experience with Istio or similar service mesh.
• Experience provisioning infrastructure on AWS, Azure, or GCP.

Culture & Benefits

• A stimulating, fast-paced environment with lots of room for creativity.
• Career development and growth, with a competitive salary.
• A flexible environment where you can control your hours (remotely) with unlimited vacation.
• Remote or Hybrid work policy.