Remote AI Infrastructure Engineer – GPU ML Ops (EMEA)
Pragmatike
Madrid
On-site
EUR 60,000 - 80,000
Full time
14 days+
Application generator
Don’t send a generic resume — generate a resume and cover letter tailored to this exact role.
Get past ATS filters
Benefits offered by this job
Work from home flexibility
Inclusive recruitment process
Opportunity to influence core engineering decisions
Job summary
Pragmatike is looking for an AI Infrastructure Engineer to join a fast-growing startup in Madrid, fully remote. The role involves building production-grade model serving infrastructure for AI systems, focusing on efficient ML inference platforms. Candidates should have 4+ years of relevant experience, skills in model serving frameworks like vLLM and TGI, and a strong background in container orchestration. Join a dynamic team to drive innovations in AI-native cloud services.
Qualifications
4+ years of experience in ML Ops, Platform Engineering, SRE, or similar infrastructure roles focused on ML systems.
Hands-on experience with model serving frameworks such as vLLM, TGI, Triton, or equivalent.
Strong background in container orchestration and operating GPU-based workloads in production.
Responsibilities
Build and operate production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton.
Design and implement robust deployment pipelines for ML models.
Develop and maintain auto-scaling systems and intelligent request routing layers.
Skills
ML Ops
Platform Engineering
SRE
Model serving frameworks (vLLM, TGI, Triton)
Container orchestration
Infrastructure-as-code tools (Terraform, Helm)
Python
Distributed systems
Performance tuning
Tools
Terraform
Helm
Kubeflow
MLflow
Job description
Pragmatike is looking for an AI Infrastructure Engineer to join a fast-growing startup in Madrid, fully remote. The role involves building production-grade model serving infrastructure for AI systems, focusing on efficient ML inference platforms. Candidates should have 4+ years of relevant experience, skills in model serving frameworks like vLLM and TGI, and a strong background in container orchestration. Join a dynamic team to drive innovations in AI-native cloud services.