Senior ML Ops Engineer — Remote (EMEA)

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

Job summary

Pragmatike is recruiting a hands-on ML Ops Engineer for a fully remote role in the EMEA timezone. You will design, build, and operate scalable ML inference platforms powering AI-native cloud services.

Responsibilities span model serving, deployment pipelines, auto-scaling, and observability. The ideal candidate combines production ML experience with strong Python and IaC skills, thriving in a distributed, remote-first startup setting.

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.
• 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

Model serving

Container orchestration

MLOps tooling

Python

Terraform

Helm

Distributed systems

AI coding assistants

Tools

Kubeflow

MLflow

KubeAI

Job description

Pragmatike is recruiting a hands-on ML Ops Engineer for a fully remote role in the EMEA timezone. You will design, build, and operate scalable ML inference platforms powering AI-native cloud services.

Responsibilities span model serving, deployment pipelines, auto-scaling, and observability. The ideal candidate combines production ML experience with strong Python and IaC skills, thriving in a distributed, remote-first startup setting.