AI Summary
We are seeking a ML Ops 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. The ideal candidate will have a strong background in container orchestration, distributed systems, and performance tuning.
Key Highlights
Design and operate production-grade model serving infrastructure
Develop and maintain auto-scaling systems and multi-model serving architectures
Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance
Key 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
Technical Skills Required
Benefits & Perks
Remote work
Competitive salary
Nice to Have
Experience with ML platforms such as Kubeflow, MLflow, or KubeAI
Knowledge of GPU scheduling, CUDA/ROCm optimization, or multi-tenant inference systems
Background in early-stage startups or greenfield infrastructure projects
Job Description
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 ML Ops 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
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- 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
- 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
Browse our curated collection of remote jobs across all categories and industries, featuring positions from top companies worldwide.
- 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.
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. You may request access, correction, or deletion of your data at any time. We are committed to maintaining the confidentiality and security of your information throughout the recruitment process.
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