AI DevOps Engineer — Mid/Senior Level

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 the Role

We are looking for a Mid-Level AI DevOps Engineer with 4-7 years of experience in DevOps, cloud infrastructure, automation, and production deployment environments. This role will focus on building, maintaining, and improving scalable infrastructure and deployment pipelines for AI and machine learning applications.

The ideal candidate 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.

Key Responsibilities

Cloud Infrastructure & DevOps

• Design, deploy, and manage

cloud-based infrastructure for AI and software applications.

• Work with cloud platforms such as AWS, Azure, or GCP.

• Build and maintain infrastructure

using tools such as Terraform, CloudFormation, Ansible.

• Support scalable, secure, and

reliable environments for production workloads.

• Optimize infrastructure for

performance, cost, availability, and operational efficiency.

CI/CD & Automation

• Build and maintain CI/CD

pipelines for application and AI service deployments.

• Automate build, testing,

deployment, and rollback processes.

• Improve deployment reliability

and reduce manual operational tasks.

• Work with tools such as Azure

DevOps, GitHub Actions, Jenkins.

• Create reusable scripts,

templates, and automation workflows for engineering teams.

Containerization & Orchestration

• Deploy and manage containerized

applications using Docker.

• Work with Kubernetes for

application deployment, scaling, networking, and troubleshooting.

• Manage Helm charts and Kubernetes

manifests.

• Support production deployments

and ensure application availability.

• Troubleshoot container, cluster,

and infrastructure-related issues.

AI / MLOps Support

• Support deployment and monitoring

of AI/ML models in production environments.

• Collaborate with data scientists,

ML engineers, and backend engineers to streamline model deployment workflows.

• 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 AI

services, APIs, and model inference workloads.

Monitoring, Logging & Reliability

• Implement and maintain

monitoring, logging, tracing, and alerting systems.

• Use tools such as Prometheus,

Grafana, ELK Stack, Datadog, New Relic, CloudWatch, or Azure Monitor.

• Monitor application and

infrastructure performance.

• Participate in incident response,

root cause analysis, and production support.

• Help improve system reliability,

uptime, and operational visibility.

Security & Compliance

• Apply DevSecOps practices across

infrastructure and deployment pipelines.

• Manage access controls, IAM

roles, secrets, and secure configuration.

• Support vulnerability scanning,

patching, and security hardening.

• Ensure cloud and deployment

environments follow security best practices.

• Work with tools such as HashiCorp

Vault, AWS Secrets Manager, Azure Key Vault, or GCP Secret Manager.

Required Qualifications

• 4-7 years of experience in DevOps, Cloud Engineering,

Site Reliability Engineering, Platform Engineering, or Infrastructure Engineering.

• Strong hands-on experience with

at least one cloud platform: AWS, Azure, or GCP.

• Experience building and managing

CI/CD pipelines.

• Strong experience with Docker and containerized deployments.

• Working experience with Kubernetes in production or near-production environments.

• Experience with

infrastructure-as-code tools such as Terraform, Ansible, CloudFormation.

• Strong scripting skills using Python,

Bash, or PowerShell.

• Experience with monitoring and

logging tools such as Prometheus, Grafana, ELK, Datadog, New Relic, or CloudWatch.

• Good understanding of networking,

Linux systems, security, and cloud architecture.

• Familiarity with AI/ML workflows,

model deployment, or MLOps concepts.

• Experience supporting production

applications and troubleshooting infrastructure issues.

Preferred Qualifications

• Experience supporting AI/ML

applications or model deployment pipelines.

• Exposure to LLM applications,

vector databases, RAG pipelines, or generative AI infrastructure.

• Experience with GPU-based

workloads or AI inference infrastructure.

• Familiarity with tools such as MLflow,

Kubeflow, SageMaker, Vertex AI, Azure ML, Airflow, or Argo Workflows.

• Experience with Helm, service

mesh, or Kubernetes operators.

• Knowledge of DevSecOps practices

and cloud security controls.

• Cloud, Kubernetes, or DevOps

certifications are a plus.

Required Technical Skills

Cloud Platforms: 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