Principal ML Ops Engineer

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

📋 Description

• Develop and manage production-grade model serving infrastructure utilizing frameworks like vLLM, TGI, Triton, or similar alternatives.

• Create and implement reliable deployment pipelines featuring blue/green and canary rollout strategies for machine learning models.

• Design and uphold auto-scaling systems, multi-model serving architectures, and intelligent request routing layers.

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

• Create observability systems for monitoring inference latency, throughput, GPU usage, cost metrics, and overall system health.

• Oversee model registries and CI/CD pipelines to facilitate automated and reproducible model deployments.

• Manage the entire lifecycle of ML systems from development to production, including operational support and on-call duties.

• Establish engineering best practices and contribute to platform scalability within a dynamic startup environment.

⛳️ Requirements

• A minimum of 4 years of experience in ML Ops, Platform Engineering, Site Reliability Engineering, or comparable infrastructure roles focused on ML systems.

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

• A solid background in container orchestration and managing GPU-based workloads in production settings.

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

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

• Strong comprehension of distributed systems, performance optimization, and production reliability engineering.

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

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

🏝️ Benefits

• Take charge of critical infrastructure that supports a rapidly expanding AI-native cloud platform.

• Construct foundational ML inference systems from the ground up in a high-growth, well-funded startup environment.

• Operate at the intersection of distributed systems, GPU computing, and sustainable cloud architecture.

• Acquire profound expertise in next-generation AI infrastructure and large-scale model serving systems.

• Play a key role in shaping core engineering decisions and establishing best practices that will scale with the organization.