DevOps Engineer – AI Image Editing SaaS Platform (Kubernetes + GPU + Cloud)
SuperDNA 3D Lab
Date: 1 week ago
City: Chandīgarh, Chandigarh
Contract type: Part time
Remote
We’re seeking a DevOps Engineer with hands-on experience managing GPU infrastructure, Kubernetes, and hybrid cloud environments (bare-metal, AWS, GCP). You’ll work closely with AI researchers and full-stack developers to build and scale the infrastructure that powers our image-processing microservices.
Responsibilities
Manage and optimize Kubernetes clusters across bare-metal servers, AWS (EKS), and GCP (GKE)
Deploy and maintain GPU-enabled workloads for AI inference and training (NVIDIA drivers, nvidia-docker, MIG configs)
Create and maintain CI/CD pipelines (GitHub Actions, ArgoCD, etc.) to automate deployments and model rollouts
Implement scalable, fault-tolerant infrastructure for AI microservices, using Celery, Redis, and FastAPI
Monitor system performance, resource utilization (CPU/GPU), and model latency
Set up and manage persistent storage (MinIO, S3), secrets, and config maps securely
Develop monitoring and alerting systems for both infrastructure and AI pipelines
Collaborate with AI engineers to support experimentation, benchmarking, and model updates
Required Skills
Solid experience with Kubernetes, particularly in GPU scheduling and resource management
Experience deploying and tuning AI/ML workloads on GPUs (NVIDIA Docker, CUDA stack, drivers)
Comfortable managing hybrid cloud infrastructure: bare-metal servers, AWS, and GCP
Deep knowledge of Docker, Helm,
Strong scripting skills (Bash, Python) for automation and tooling
Experience with Redis, Celery, and handling message queues or background job systems
Tech Stack
Infra: Docker, Kubernetes, Helm, Terraform, GitHub Actions
Cloud: AWS (EKS, EC2, S3), GCP (GKE, Compute), Bare-Metal Servers
AI Ops: NVIDIA Docker, CUDA, Celery, Redis, FastAPI
Storage: MinIO, AWS S3, Persistent Volumes
Responsibilities
Manage and optimize Kubernetes clusters across bare-metal servers, AWS (EKS), and GCP (GKE)
Deploy and maintain GPU-enabled workloads for AI inference and training (NVIDIA drivers, nvidia-docker, MIG configs)
Create and maintain CI/CD pipelines (GitHub Actions, ArgoCD, etc.) to automate deployments and model rollouts
Implement scalable, fault-tolerant infrastructure for AI microservices, using Celery, Redis, and FastAPI
Monitor system performance, resource utilization (CPU/GPU), and model latency
Set up and manage persistent storage (MinIO, S3), secrets, and config maps securely
Develop monitoring and alerting systems for both infrastructure and AI pipelines
Collaborate with AI engineers to support experimentation, benchmarking, and model updates
Required Skills
Solid experience with Kubernetes, particularly in GPU scheduling and resource management
Experience deploying and tuning AI/ML workloads on GPUs (NVIDIA Docker, CUDA stack, drivers)
Comfortable managing hybrid cloud infrastructure: bare-metal servers, AWS, and GCP
Deep knowledge of Docker, Helm,
Strong scripting skills (Bash, Python) for automation and tooling
Experience with Redis, Celery, and handling message queues or background job systems
Tech Stack
Infra: Docker, Kubernetes, Helm, Terraform, GitHub Actions
Cloud: AWS (EKS, EC2, S3), GCP (GKE, Compute), Bare-Metal Servers
AI Ops: NVIDIA Docker, CUDA, Celery, Redis, FastAPI
Storage: MinIO, AWS S3, Persistent Volumes
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