Principal ML Ops Engineer (EMEA Remote)

2 months ago

Prague, Czechia +7 moreStaff+

Responsibilities

Build and operate production-grade model-serving infrastructure using vLLM, TGI, Triton, or equivalent frameworks.
Design deployment pipelines with blue/green and canary rollout strategies for ML models.
Develop autoscaling systems, multi-model serving architectures, and intelligent request-routing layers.
Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance.
Design observability for inference latency, throughput, GPU usage, cost metrics, and system health.
Manage model registries and automated, reproducible model deployment pipelines.
Own the ML systems lifecycle from development through production, including operational support and on-call responsibilities.
Define engineering best practices and contribute to platform scalability.

At least four years of experience in MLOps, 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 experience with container orchestration and production GPU-based workloads.
Experience with MLOps tooling, including model registries, experiment tracking, and automated deployment pipelines.
Proficiency in Python and infrastructure-as-code tools such as Terraform and Helm.
Strong understanding of distributed systems, performance tuning, and production reliability engineering.
Ability to use AI coding assistants for development and debugging workflows.
Preferred experience with Kubeflow, MLflow, or KubeAI.
Preferred knowledge of GPU scheduling, CUDA or ROCm optimization, and multi-tenant inference systems.
Preferred experience with GPU cost optimization, early-stage startups, greenfield infrastructure, and production systems built from scratch.
Fluent English is required.

Fully remote role for candidates working in EMEA time zones
ASAP start date
Opportunity to build foundational ML inference infrastructure from the ground up
Work at the intersection of distributed systems, GPU computing, and sustainable cloud architecture
Influence core engineering decisions and define scalable engineering best practices

11-50 employees

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