ML Ops Engineer (EMEA Remote)

You will be responsible for designing and operating the core infrastructure that serves machine learning models at scale. You will work closely with infrastructure, platform, and applied AI teams to ensure high availability, low latency, and cost-efficient inference systems. Strong ownership, production mindset, and experience with distributed GPU systems are essential.

Your 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

Required 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

Preferred Qualifications

Experience with ML platforms such as Kubeflow, MLflow, or KubeAI

Knowledge of GPU scheduling, CUDA/ROCm optimization, or multi-tenant inference systems

Experience with cost optimization across different GPU types and inference workloads

Background in early-stage startups or greenfield infrastructure projects

Proven experience building production systems from scratch rather than maintaining legacy platforms

Why Join Us

Take ownership of critical infrastructure powering a rapidly scaling AI-native cloud platform

Build foundational ML inference systems from the ground up in a high-growth, well-funded startup

Work at the intersection of distributed systems, GPU computing, and sustainable cloud architecture

Gain deep expertise in next-generation AI infrastructure and large-scale model serving systems

Influence core engineering decisions and define best practices that will scale with the company.

Pragmatike is committed to a fair, transparent, and inclusive recruitment process. We do not discriminate based on age, disability, gender, gender identity or expression, marital or civil partner status, pregnancy or maternity, race, religion or belief, sex, or sexual orientation.

In accordance with GDPR, your personal data will be processed lawfully, fairly, and securely, and used solely for recruitment purposes, including sharing it with our client(s) for employment consideration.