AI DevOps Engineer — Mid/Senior Level

Stackular; Hyderabad; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

Stackular invites an experienced AI DevOps Engineer to join its Hyderabad on-site team. You will implement cloud infrastructure, CI/CD pipelines, and AI deployment workflows, collaborating with data scientists and backend engineers. Strong Kubernetes, Docker, and IaC skills are essential.

Ideal candidates bring 4-7 years in DevOps or platform engineering, with hands-on cloud experience and a proactive approach to reliability, security, and monitoring in production environments.

Qualifications

• 4-7 years of experience in DevOps, cloud engineering, platform engineering, or SRE.
• Hands-on with at least one cloud platform: AWS, Azure, or GCP.
• Experience building and managing CI/CD pipelines.
• Strong Docker and containerized deployments experience.
• Kubernetes in production or near-production environments.
• Infrastructure-as-code with Terraform, Ansible, CloudFormation.
• Scripting with Python, Bash, or PowerShell.
• Experience with monitoring/logging tools: Prometheus, Grafana, ELK, Datadog, New Relic, CloudWatch.
• Understanding of networks, Linux systems, security, and cloud architecture.
• Familiarity with AI/ML workflows, model deployment, or MLOps concepts.
• Experience supporting production apps and troubleshooting infra issues.

Responsibilities

• Design, deploy, and manage cloud-based infrastructure for AI and software apps.
• Work with cloud platforms such as AWS, Azure, or GCP.
• Build and maintain infrastructure using Terraform, CloudFormation, Ansible.
• Support scalable, secure, reliable production environments.
• Optimize infrastructure for performance, cost, availability, and ops efficiency.
• Build and maintain CI/CD pipelines for app and AI service deployments.
• Automate build, tests, deployment, and rollback processes.
• Improve deployment reliability and reduce manual tasks.
• Work with Azure DevOps, GitHub Actions, Jenkins; create reusable scripts and templates.
• Deploy and manage containerized apps with Docker; use Kubernetes; manage Helm charts.

Skills

Cloud platforms

CI/CD pipelines

Docker

Kubernetes

Infrastructure as Code

Python scripting

Monitoring tools

Linux systems

Security basics

Tools

Terraform

Ansible

CloudFormation

Jenkins

Azure DevOps

GitHub Actions

Helm

Prometheus

Grafana

ELK

Datadog

New Relic

CloudWatch

MLflow

Kubeflow

SageMaker

Vertex AI

Airflow

Argo Workflows

Job description

A different kind of consulting company that delivers offshore and nearshore product development, user experience, app modernization, business intelligence, and business apps services.

Job Description

Job Title: AI DevOps Engineer —Mid/Senior Level
Experience: 4–7 Years
Location: Raidurg Main Road, Hyderabad.
Work Mode: On-site
Work Hours: 2-11 PM
Notice Period: Immediate Joiner (15-30 days)

About theRole

We arelooking for a Mid-Level AI DevOps Engineer with 4-7 years ofexperience in DevOps, cloud infrastructure, automation, and productiondeployment environments.

The idealcandidate should have strong hands-on experience with cloud platforms,CI/CD, Docker, Kubernetes, infrastructure as code, monitoring, and automation,along with a working understanding of AI/ML deployment workflows.

KeyResponsibilities

CloudInfrastructure & DevOps

• Design, deploy, and managecloud-based infrastructure for AI and software applications.
  
• Work with cloud platforms such as AWS, Azure, or GCP.
  
• Build and maintain infrastructureusing tools such as Terraform, CloudFormation, Ansible.
  
• Support scalable, secure, andreliable environments for production workloads.
  
• Optimize infrastructure forperformance, cost, availability, and operational efficiency.
  

CI/CD& Automation

• Build and maintain CI/CDpipelines for application and AI service deployments.
  
• Automate build, testing,deployment, and rollback processes.
  
• Improve deployment reliabilityand reduce manual operational tasks.
  
• Work with tools such as AzureDevOps, GitHub Actions, Jenkins.
  
• Create reusable scripts,templates, and automation workflows for engineering teams.
  

Containerization& Orchestration

• Deploy and manage containerizedapplications using Docker.
  
• Work with Kubernetes forapplication deployment, scaling, networking, and troubleshooting.
  
• Manage Helm charts and Kubernetesmanifests.
  
• Troubleshoot container, cluster,and infrastructure-related issues.
  

AI / MLOpsSupport

• Support deployment and monitoringof AI/ML models in production environments.
  
• Collaborate with data scientists,ML engineers, and backend engineers to streamline model deploymentworkflows.
  
• Assist with model versioning,model serving, and release automation.
  
• Work with MLOps tools such as MLflow,Kubeflow, SageMaker, Vertex AI, Azure ML, Airflow, or similar platforms.
  
• Support infrastructure for AIservices, APIs, and model inference workloads.
  
• Implement and maintainmonitoring, logging, tracing, and alerting systems.
  
• Use tools such as Prometheus,Grafana, ELK Stack, Datadog, New Relic, CloudWatch, or Azure Monitor.
  
• Monitor application andinfrastructure performance.
  
• Participate in incident response,root cause analysis, and production support.
  
• Help improve system reliability,uptime, and operational visibility.
  

Security& Compliance

• Apply DevSecOps practices acrossinfrastructure and deployment pipelines.
  
• Manage access controls, IAMroles, secrets, and secure configuration.
  
• Support vulnerability scanning,patching, and security hardening.
  
• Ensure cloud and deploymentenvironments follow security best practices.
  
• Work with tools such as HashiCorpVault, AWS Secrets Manager, Azure Key Vault, or GCP Secret Manager.
  

RequiredQualifications

• 4-7 years of experience in DevOps, Cloud Engineering,Site Reliability Engineering, Platform Engineering, or InfrastructureEngineering.
  
• Strong hands-on experience withat least one cloud platform: AWS, Azure, or GCP.
  
• Experience building and managingCI/CD pipelines.
  
• Strong experience with Docker and containerized deployments.
  
• Working experience with Kubernetes in production or near-production environments.
  
• Experience withinfrastructure-as-code tools such as Terraform, Ansible, CloudFormation.
  
• Strong scripting skills using Python,Bash, or PowerShell.
  
• Experience with monitoring andlogging tools such as Prometheus, Grafana, ELK, Datadog, New Relic, orCloudWatch.
  
• Good understanding of networking,Linux systems, security, and cloud architecture.
  
• Familiarity with AI/ML workflows,model deployment, or MLOps concepts.
  
• Experience supporting productionapplications and troubleshooting infrastructure issues.
  

PreferredQualifications

• Experience supporting AI/MLapplications or model deployment pipelines.
  
• Exposure to LLM applications,vector databases, RAG pipelines, or generative AI infrastructure.
  
• Experience with GPU-basedworkloads or AI inference infrastructure.
  
• Familiarity with tools such as MLflow,Kubeflow, SageMaker, Vertex AI, Azure ML, Airflow, or Argo Workflows.
  
• Experience with Helm, servicemesh, or Kubernetes operators.
  
• Knowledge of DevSecOps practicesand cloud security controls.
  
• Cloud, Kubernetes, or DevOpscertifications are a plus.
  

Required TechnicalSkills

CloudPlatforms: AWS,Azure, GCP
Containers & Orchestration: Docker, Kubernetes, Helm
Infrastructure as Code: Terraform, Ansible, CloudFormation
CI/CD: GitHub Actions, Jenkins, Azure DevOps
Scripting: Python, Bash, PowerShell
Monitoring & Logging: Prometheus, Grafana, ELK Stack, Datadog, New Relic, CloudWatch
MLOps / AI Tools: MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML,Airflow
Security: IAM, secrets management, vulnerability scanning, DevSecOps
Operating Systems: Linux, Unix-based systems