ML Ops Engineer

This is a fully remote position, open to applicants in Czechia.

📋 Description

• Construct and manage production-grade model serving infrastructure utilizing frameworks such as vLLM, TGI, Triton, or similar alternatives.

• Design and execute robust deployment pipelines incorporating blue/green and canary rollout strategies for machine learning models.

• Develop and sustain auto-scaling systems, multi-model serving frameworks, and intelligent request routing layers.

• Enhance GPU usage, memory efficiency, network throughput, and performance of model artifact storage.

• Create observability systems to monitor inference latency, throughput, GPU utilization, cost metrics, and the overall health of the system.

• Manage model registries and CI/CD pipelines that facilitate automated and reproducible model deployments.

• Oversee the complete lifecycle of machine learning systems from development to production, including operational support and on-call duties.

• Establish engineering best practices and contribute to the scalability of the platform within a dynamic startup atmosphere.

⛳️ Requirements

• A minimum of 4 years of experience in ML Ops, Platform Engineering, SRE, or related infrastructure roles with a focus on machine learning systems.

• Practical experience with model serving frameworks such as vLLM, TGI, Triton, or equivalent.

• Strong expertise in container orchestration and managing GPU-based workloads in a production environment.

• Familiarity with MLOps tools, including model registries, experiment tracking, and automated deployment pipelines.

• Proficient in Python and infrastructure-as-code tools (e.g., Terraform, Helm, or similar).

• Deep understanding of distributed systems, performance optimization, and engineering for production reliability.

• Capability to effectively utilize AI coding assistants to enhance development and debugging processes.

• An ownership mindset with the ability to work independently in a remote-first setting.

🏝️ Benefits

• Agile working environment.

• Opportunities for personal development.

• Flexible working hours.

• Options for remote work.