Cloud Infrastructure/DevOps Engineer

About Avey

Position Overview

Key Responsibilities

Cloud Infrastructure & Automation

• Design, build, and manage cloud infrastructure (AWS and Azure) using Infrastructure-as-Code (IaC) tools such as Terraform and Ansible.
• Automate provisioning, scaling, and configuration of compute, storage, and networking resources.
• Implement and maintain container orchestration platforms such as Kubernetes, OpenShift, and Docker Swarm for scalable deployments.
• Develop and maintain GitOps workflows using ArgoCD, GitHub Actions for automated CI/CD processes.
• Build and operate MLOps pipelines to support the training, deployment, versioning, and monitoring of AI APIs and Large Language Models (LLMs) in production, leveraging tools such as MLflow, Kubeflow, and Azure ML / Vertex AI.
• 2.

CI/CD Pipeline Development

• Design and manage end-to-end CI/CD pipelines for application, data, and AI workloads.
• Automate build, test, and deployment processes across environments (Dev, Test, Prod).
• Integrate pipelines with code scanning, static analysis, and container security tools.
• Implement blue-green and canary deployments to ensure zero-downtime releases.
• 3.

Monitoring, Observability & Reliability

• Implement comprehensive monitoring, logging, and alerting using tools such as OpenObserve, Azure Monitor, Log Analytics.
• Develop automated alerts and dashboards for real-time performance, resource utilization, and cost monitoring.
• Participate in incident response, root cause analysis, and performance optimization of infrastructure and pipelines.
• 4.

Security, Compliance & Governance

• Partner with the InfoSec team to integrate Security-as-Code and compliance controls within DevOps workflows.
• Manage secrets, keys, and credentials using Azure Key Vault, and HashiCorp Vault.
• Ensure compliance with ISO 27001, NIA Qatar, CIS Benchmarks, and corporate governance standards.
• Implement network security controls (NSGs, firewalls, WAFs, VNETs) and enforce Zero Trust principles across CI/CD deployments.

• Collaboration & Continuous Improvement

• Partner with Data Science and AI teams to operationalize model training and deployment pipelines (MLOps).
• Collaborate with development teams to containerize and deploy cloud-native applications.
• Identify and implement process improvements to enhance release velocity and reduce deployment risk.
• Stay abreast of emerging technologies and DevOps trends to continuously evolve automation maturity.

Requirements

• :
• 4-year STEM degree or related field.
• 4+ years as a dedicated Infrastructure or DevOps Engineer.
• Proven expertise in CI/CD pipelines, automation, and cloud infrastructure.
• Strong hands-on experience with at least one major cloud provider.
• Deep, hands-on Kubernetes expertise (must-have).
• Production experience with EKS and AKS.
• Cluster design, Helm, operators, and CRDs.
• Service mesh (Istio or Linkerd).
• Autoscaling (HPA, VPA, KEDA).
• Networking: CNI, ingress controllers, Gateway API, network policies.
• Nice to Have
• :
• Healthcare Experience (HIS/EMR systems, Cerner is a huge plus).
• Prior GCC Experience (NIA Qatar, HIPAA, or GDPR)
• Experience working in a Startup environment.
• DevOps/MLOps for GPU inference.
• GPU on Kubernetes (NVIDIA GPU Operator, MIG).
• LLM serving: Experience with vLLM, TGI, SGLang, TensorRT-LLM, NVIDIA Dynamo.
• Autoscaling inference APIs.
• Experience with Ray
• GPU cost optimization (spot, Karpenter).
• CKA/CKAD/CKS or AWS/Azure certs.

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