AI Infrastructure Engineer (GPU) - Remote EMEA
At a glance
Mid-level ML Platform & Ops role at Pragmatike. Remote, Ukraine · full-time.
Pay not stated ML Platform & Ops salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and run production model-serving systems with vLLM, TGI, Triton or comparable frameworks
- Create deployment pipelines that release models through blue-green and canary rollouts
- Develop autoscaling, multi-model serving and request-routing systems for inference workloads
- Tune GPU use, memory, network capacity and model storage, while tracking latency, throughput, cost and system health
- Manage model registries and deployment automation, and support ML systems through production operations and on-call
What you bring
- At least four years in ML operations, platform engineering, SRE or a similar infrastructure role focused on ML
- Hands-on work with model-serving frameworks such as vLLM, TGI or Triton, plus production GPU workloads and container orchestration
- Experience with model registries, experiment tracking and automated deployment, alongside Python and infrastructure-as-code tools
- A strong grounding in distributed systems, performance tuning and reliability, and the ability to use AI coding assistants effectively
Who this fits
This hands-on mid-level role suits an engineer who can independently build and operate production ML infrastructure. It is fully remote for someone working in the EMEA timezone, requires fluent English, and includes operational support and on-call duties.
From the employer
Location: Fully remote (EMEA timezone)
Start date: ASAP
Languages: Fluent English required
Industry: Cloud Computing / AI / European Deep-Tech SaaS
About the Role
Pragmatike is recruiting on behalf of a fast-scaling, well-funded distributed cloud infrastructure startup building next-generation AI-native cloud services. The company is redefining how compute is delivered by providing GPU-powered infrastructure for AI/ML workloads, secure storage, and high-speed data transfer through a decentralized architecture that significantly reduces environmental impact compared to traditional cloud providers.
We are seeking a AI Infrastructure Engineer with strong experience in production-grade model serving and infrastructure for AI systems. This is a highly technical, hands-on role focused on building scalable, reliable, and efficient ML inference platforms powering real-time AI applications.
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.