Job Overview
Location
Ukraine
Job Type
Full-time
Category
Machine Learning Engineer
Date Posted
August 8, 2026
Full Job Description
📋 Description
- • We are seeking a highly technical ML Ops Engineer with strong experience in production-grade model serving and infrastructure for AI systems.
- • The ideal candidate will have hands-on experience with model serving frameworks such as vLLM, TGI, Triton, or equivalent.
- • 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.
- • You will be responsible for building and operating production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent.
- • You will design and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models.
- • You will develop and maintain auto-scaling systems, multi-model serving architectures, and intelligent request routing layers.
- • You will optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance.
- • You will design observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health.
- • You will manage model registries and CI/CD pipelines enabling automated and reproducible model deployments.
- • You will own the full lifecycle of ML systems from development through production, including operational support and on-call responsibilities.
- • You will define engineering best practices and contribute to platform scalability in a fast-moving startup environment.
🎯 Requirements
- • 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.
🏖️ Benefits
- • 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.
Skills & Technologies
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About Pragmatike Soluciones Tecnológicas S.L.
Spanish technology firm founded in 2014, delivering custom software, mobile apps, cloud migration, and data analytics. Combines agile development, AI, and DevOps practices to serve finance, healthcare, retail, and public sectors across Europe and Latin America. Core services include UX/UI design, QA automation, and 24/7 managed support, with ISO 27001-certified processes and multilingual teams in Madrid, Barcelona, and remote hubs.
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