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Senior DevOps Engineer, MLOps Focus (Remote)

Flatgigs
Remote
Islamabad Capital Territory, PakistanRemote· 3 months ago

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Senior DevOps Engineer (MLOps Focus)

Location: Islamabad Capital Territory, Pakistan

Workplace: remote

Employment Type: full

Description

At AHOY, we reimagine and redefine movement. We’re building a next-generation technology ecosystem across mobility, logistics, and infrastructure, enabling innovators to create, scale, and operate smarter systems.

We’re looking for a hands-on Senior DevOps Engineer to help us build, scale, and secure our multi-cloud infrastructure.

This role sits at the intersection of DevOps, AI infrastructure, and cloud security, where you’ll take ownership of systems powering advanced machine learning workloads. As part of a fast-moving engineering team, you’ll also play a key role in supporting internal IT operations, ensuring reliability across both platform and people.

What You’ll Work on

  • Design and maintain secure, scalable cloud infrastructure across Azure and GCP
  • Build and optimise CI/CD pipelines and deployment workflows
  • Implement and manage Infrastructure as Code (IaC) (Terraform or similar)
  • Deploy and operate machine learning pipelines in production environments
  • Manage and scale multi-cluster GPU environments for AI workloads
  • Set up monitoring, logging, and alerting systems to ensure reliability
  • Apply cloud security best practices across infrastructure and access layers
  • Troubleshoot and resolve system, deployment, and performance issues
  • Provide hands-on IT support (user access, devices, internal systems) to ensure smooth day-to-day operations
  • Collaborate with engineering teams to improve scalability, performance, and automation

Requirements

  • 4+ years of experience in DevOps / SRE / Infrastructure Engineering
  • Strong experience with Azure and Google Cloud Platform (GCP)
  • Hands-on experience building and managing CI/CD pipelines
  • Solid expertise in Infrastructure as Code (Terraform, Pulumi, etc.)
  • Experience managing Kubernetes environments and GPU workloads
  • Proven experience deploying machine learning models or MLOps pipelines
  • Strong understanding of cloud security, networking, and system reliability
  • Comfortable handling IT support responsibilities alongside core DevOps work
  • Strong problem-solving mindset with the ability to work independently

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