Senior DevOps Engineer (MLOps Focus)
At a glance
Senior ML Platform & Ops role at Flatgigs. Remote, Pakistan · full-time.
Pay not stated ML Platform & Ops salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and maintain secure cloud foundations on Azure and Google Cloud for machine learning workloads
- Create CI/CD workflows and manage infrastructure through Terraform or comparable tools
- Run production ML pipelines and scale Kubernetes clusters equipped with GPUs
- Set up monitoring, logs and alerts, then resolve platform, deployment and performance problems
- Handle user access, devices and internal systems while improving automation with engineering teams
What you bring
- At least four years in DevOps, SRE or infrastructure engineering
- Strong working knowledge of Azure and Google Cloud, plus hands-on CI/CD and infrastructure-as-code experience
- Experience operating Kubernetes and GPU workloads, and putting ML models or pipelines into production
- A solid grasp of cloud security, networking and reliability, with the ability to work independently
Who this fits
This senior, hands-on role suits someone who can balance MLOps and cloud infrastructure work with internal IT support. It is remote in Pakistan.
From the employer
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