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Proto

DevOps

Posted 29 minutes ago
2-5 years experience
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AI Summary

You will build and maintain resilient cloud infrastructure, GPU environments, and automated release pipelines while supporting enterprise and on-premise deployments. Additionally, you will manage Kubernetes clusters and collaborate with teams to enforce DevSecOps best practices and infrastructure security standards.

The role

Proto is hiring a DevOps Engineer to manage, scale, and secure the cloud and infrastructure powering our AI agent platform – products that put AI agents in the hands of governments, regulators, and businesses across emerging markets.

You will own infrastructure as code, automated CI/CD pipelines, cloud infrastructure, and site reliability from day one. You will also play an important role in deploying and managing GPU infrastructure for AI workloads, supporting LLM and other AI model deployments, and helping enterprise clients implement Proto's platform within their own on-premise environments.

You will work in a lean, remote-first team where infrastructure improvements, AI deployments, and customer implementations happen in days, not quarters.

What you will do

You will build and maintain resilient cloud infrastructure, GPU environments, and automated release pipelines while supporting enterprise and on-premise deployments.

  • Design, build, and maintain scalable cloud infrastructure using Infrastructure as Code (IaC) tools like Terraform.
  • Develop, automate, and optimize robust CI/CD deployment pipelines for high availability and zero-downtime releases.
  • Manage Kubernetes clusters, container orchestration, and microservices deployment across cloud and on-premise environments.
  • Deploy, configure, and optimize GPU infrastructure for AI workloads, including GPU-enabled servers, NVIDIA GPUs, CUDA, and GPU drivers.
  • Support deployment and operation of AI models, LLM inference services, and GPU-accelerated applications.
  • Assist with infrastructure sizing, GPU resource allocation, and performance optimization for AI model serving and inference workloads.
  • Lead and support customer deployments in on-premise and private cloud environments, including restricted or air-gapped networks.
  • Package, configure, and deploy containerized applications using Docker and Docker Compose, including Kubernetes-based deployments.
  • Work with customer IT and infrastructure teams to troubleshoot deployment issues involving networking, firewalls, proxies, storage, and system dependencies.
  • Implement monitoring, logging, tracing, and alerting systems to ensure high service uptime and performance.
  • Drive Site Reliability Engineering (SRE) practices, incident management, and automated failover and recovery processes.
  • Collaborate with development, security, and QA teams to enforce DevSecOps best practices and infrastructure security standards.

Proto | DevOps Engineer

What you bring

You are an infrastructure expert who excels in cloud architecture, automation, system reliability, and deploying production workloads across different environments.

  • 3+ years' professional experience in DevOps, Cloud Engineering, Infrastructure Engineering, or Site Reliability Engineering (SRE).
  • Deep expertise with cloud providers (AWS), Kubernetes container orchestration, and Docker containers.
  • Proven track record implementing Infrastructure as Code (Terraform) and maintaining automated CI/CD pipelines.
  • Hands-on experience managing Linux servers and production infrastructure.
  • Experience deploying and maintaining applications across cloud, private cloud, or on-premise environments.
  • Strong troubleshooting skills across infrastructure, networking, storage, and application deployment environments.

Core Technical Skills

  • Strong scripting abilities in Python and Bash for infrastructure automation and tooling.
  • Expertise in Docker, Kubernetes, container orchestration, and deployment automation.
  • Strong understanding of AWS infrastructure, networking, IAM, security groups, VPCs, load balancing, and storage services.
  • Experience managing and tuning relational databases (PostgreSQL, MySQL) and cache layers (Redis).
  • Proficiency with monitoring, observability, and log aggregation tools (Prometheus, Grafana, Datadog, ELK).
  • Hands-on experience with service mesh, ingress control, and networking fundamentals (DNS, VPN, load balancing, firewalls, and reverse proxies).
  • Familiarity with Linux administration, system performance tuning, and resource management.

Nice to have

None of these is essential, but each will help you hit the ground running.

  • Hands-on experience deploying and managing GPU infrastructure, particularly NVIDIA GPUs (L4, T4, A10, A100, H100, H200, or similar).
  • Familiarity with NVIDIA CUDA, NVIDIA Container Toolkit, GPU drivers, and GPU monitoring tools such as DCGM.
  • Experience deploying AI/ML models and LLM inference frameworks such as vLLM, Ollama, Triton Inference Server, or Hugging Face TGI.
  • Experience with GPU resource scheduling, multi-GPU deployments, and optimizing GPU utilization.
  • Previous experience supporting enterprise on-premise deployments, private cloud environments, or air-gapped infrastructure.
  • Familiarity with Linux GPU server configuration and troubleshooting, including CUDA compatibility and driver management.
  • Experience with VMware, Proxmox, or other virtualization platforms.
  • Understanding of security hardening, network isolation, and deployment requirements for regulated environments.
  • Background in natural language processing or computational linguistics.
  • Previous experience at a SaaS or AI company.
  • Previous experience deploying AI models.
  • Fluency in multiple languages.

Proto | DevOps Engineer

Why Proto

Proto is a Canadian-registered AI communication platform company deploying AI agents for governments, regulators, and private sector organisations across emerging markets. The work is real, the impact is public, and the team is small enough that you will shape what we build.

  • Real-world impact: Your code serves citizens, institutions, and businesses in markets where good technology changes daily life.
  • Ownership: A lean team means broad scope, fast decisions, and visible results.
  • Global reach: Work with clients and colleagues across multiple countries and regions.
  • Growth: Learn from the frontier of applied AI, with room to grow into technical leadership.
  • Remote-first: We have worked remotely since the start, with a tested process for team collaboration that maximises productivity.

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