Remote ML Ops Engineer - Scalable GPU Inference

Pragmatike; Italy; Remote; EUR 90,000 - 130,000; Full time

Job summary

Pragmatike is recruiting for a remote‑first ML infrastructure role within a fast‑scaling European cloud platform. The role focuses on designing and operating core ML inference infrastructure at scale, collaborating with infrastructure, platform, and AI teams to ensure high availability, low latency, and cost efficiency.

You will own deployment pipelines, observability, GPU utilization, and model registries while advancing production reliability and platform scalability in a remote‑first startup

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
• Experience with MLOps tooling including model registries, experiment tracking, and automated deployment pipelines
• Proficiency in Python and infrastructure‑as‑code tools (e.g., Terraform, Helm, or similar)
• Strong understanding of distributed systems, performance tuning, and production reliability engineering
• Ability to effectively use AI coding assistants to accelerate development and debugging workflows
• Ownership mindset with the ability to operate independently in a remote‑first environment

Responsibilities

• Build and operate production‑grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent
• Design and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models
• Develop and maintain auto‑scaling systems, multi‑model serving architectures, and intelligent request routing layers
• Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance
• Design observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health
• Manage model registries and CI/CD pipelines enabling automated and reproducible model deployments
• Own the full lifecycle of ML systems from development through production, including operational support and on‑call responsibilities
• Define engineering best practices and contribute to platform scalability in a fast‑moving startup environment

Skills

ML Ops

Platform Engineering

SRE

Python

CI/CD

Distributed systems

Remote collaboration

Tools

vLLM

TGI

Triton

Kubernetes

Terraform

Helm

Job description

Pragmatike is recruiting for a remote‑first ML infrastructure role within a fast‑scaling European cloud platform. The role focuses on designing and operating core ML inference infrastructure at scale, collaborating with infrastructure, platform, and AI teams to ensure high availability, low latency, and cost efficiency.

You will own deployment pipelines, observability, GPU utilization, and model registries while advancing production reliability and platform scalability in a remote‑first startup