ML Ops Engineer at Pragmatike

Department: Work with our Clients

Setup: Remote - Ukraine
Type: Full-time
Level: senior
Posted: Aug 6, 2026

Description

Pragmatike is recruiting a fully remote ML Ops Engineer for a distributed cloud infrastructure startup in the EMEA timezone. The role focuses on building and operating production-grade machine learning inference platforms, including model-serving infrastructure, deployment pipelines, auto-scaling, GPU optimization, observability, model registries, and CI/CD. Candidates need at least four years of experience in ML Ops, platform engineering, SRE, or similar infrastructure roles, along with hands-on experience with vLLM, TGI, Triton, container orchestration, Python, and infrastructure-as-code tools.

Required Skills

Model serving
Container orchestration
Python
Terraform
Distributed systems
GPU optimization
Observability
CI/CD

Additional Information

Required Previous Experiences: 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 such as Terraform or Helm
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