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Nexxa
Toronto, Canada · United States3 months ago
Grade
Staff
Experience
from 6 years
Work format
Remote, Onsite
English level
B2, upper-intermediate
An email or a Telegram message to the recruiter
Nexxa is building the best AI systems for heavy industries — enabling machines, systems, and operations to think, decide, and act autonomously across manufacturing, large-scale infrastructure, logistics, and legacy environments.
Our mission is to translate deep technical breakthroughs into operational reality, solving some of the hardest systems-level problems in industry.
We're looking for a Senior/Staff DevOps Engineer who has spent the last several years building and operating the infrastructure that lets AI and industrial systems run reliably at scale. You understand what it takes to keep production ML and data workloads fast, observable, and resilient — from GPU-backed training and inference clusters to the pipelines that connect them to real-world industrial environments.
This role is ideal for candidates who want deep infrastructure ownership at a company where uptime, latency, and reliability directly affect physical operations — not just software. You'll partner closely with AI, data, and product engineering teams to make sure the systems they build can actually run in production, safely and at scale.
Own and evolve Nexxa's core infrastructure — compute, networking, storage, and deployment systems — end-to-end
Design and operate CI/CD pipelines that support fast, safe iteration across AI, data, and product engineering teams
Build and maintain infrastructure-as-code (e.g., Terraform, Pulumi) for reproducible, auditable environments across cloud and on-prem/edge deployments
Architect and manage Kubernetes-based platforms for training, inference, and application workloads, including GPU scheduling and autoscaling
Partner with data and AI teams to support the infrastructure behind:
Define and drive observability practices — metrics, logging, tracing, and alerting — across distributed systems
Establish and enforce reliability practices: SLOs/SLIs, incident response, postmortems, and on-call rotations
Design for security and compliance across cloud infrastructure, secrets management, and access control, particularly relevant to industrial and legacy-environment integrations
Make pragmatic tradeoffs across cost, latency, reliability, and developer velocity
Collaborate with engineering leadership to define infrastructure roadmap and platform strategy
Mentor engineers on infrastructure best practices and raise the bar for operational excellence across the org
You can own ambiguous, high-stakes infrastructure problems end-to-end
Systems you build stay reliable as usage and scale grow — you design for the next order of magnitude, not just today
You bring strong technical judgment on tradeoffs between reliability, cost, and speed
You raise the bar for operational rigor and engineering discipline across the team
You help define what's next for the platform, not just execute what's known
Nexxa is building the best AI systems for heavy industries — enabling machines, systems, and operations to think, decide, and act autonomously across manufacturing, large-scale infrastructure, logistics, and legacy environments.
Our mission is to translate deep technical breakthroughs into operational reality, solving some of the hardest systems-level problems in industry.
We're looking for a Senior/Staff DevOps Engineer who has spent the last several years building and operating the infrastructure that lets AI and industrial systems run reliably at scale. You understand what it takes to keep production ML and data workloads fast, observable, and resilient — from GPU-backed training and inference clusters to the pipelines that connect them to real-world industrial environments.
This role is ideal for candidates who want deep infrastructure ownership at a company where uptime, latency, and reliability directly affect physical operations — not just software. You'll partner closely with AI, data, and product engineering teams to make sure the systems they build can actually run in production, safely and at scale.
If you're passionate about building the infrastructure that powers advanced AI solutions in the real world, we'd love to connect.
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