Role: Analyst
Required Skills: AWS | DevOps
Qualification: BACHELOR OF COMPUTER SCIENCE
Experience: 10 - 13 Years
Function: TECHNOLOGY
Apply By: 2026-12-25
Must Have Technical/Functional Skills
Primary Skill: Senior DevOps Engineer with strong expertise in containerization, cloud-native infrastructure, automation, and
AI/ML workload deployment.
Experience: Extensive experience delivering senior-level DevOps, cloud infrastructure, SRE, or platform engineering
solutions.
Roles & Responsibilities
Bachelor's or master's degree in computer science or a related field, or equivalent relevant professional experience.
- Design, build, and maintain scalable DevOps infrastructure across cloud and hybrid environments. Implement containerization solutions using Docker and orchestration platforms such as Kubernetes, including Helm and
Kustomize.
- Design, automate, and optimize CI/CD pipelines for application and AI/ML model deployment.
- Manage cloud infrastructure on AWS, Azure, or GCP, including networking, security, compute, and storage.
Implement Infrastructure as Code using Terraform, Ansible, or equivalent tools.
- Deploy, monitor, and scale AI/ML applications, model-serving APIs, and inference workloads using containerized
environments.
Implement MLOps workflows for model versioning, artifact management, model deployment, and monitoring.
- Support AI/LLM-based applications, including RAG and model-serving infrastructure, where applicable.
- Leverage AI-powered DevOps tools for pipeline optimization, incident analysis, root-cause investigation, and operational
automation.
Implement observability using Prometheus, Grafana, ELK/EFK, OpenTelemetry, or equivalent platforms.
- Establish security best practices, including container image scanning, secrets management, RBAC, vulnerability
management, and DevSecOps controls.
- Troubleshoot complex production issues related to containers, Kubernetes, networking, deployments, and performance.
- Mentor engineers, define DevOps standards, and collaborate with development, security, data engineering, and AI/ML
teams.
Required Skills
- Extensive experience in DevOps, cloud infrastructure, SRE, or platform engineering.
- Strong hands-on expertise in Docker, Kubernetes, container orchestration, and microservices deployment.
- Proficiency in CI/CD tools such as Jenkins, GitLab CI/CD, GitHub Actions, or Azure DevOps.
- Strong experience with at least one major cloud platform: AWS, Azure, or GCP.
- Hands-on experience with Terraform, Ansible, and Infrastructure as Code.
- Strong scripting and automation skills using Python, Bash, or PowerShell.
- Experience with Git-based workflows, artifact repositories, and container registries.
- Understanding of networking, Linux administration, load balancing, DNS, TLS, and distributed systems.
- Experience implementing monitoring, logging, alerting, high availability, disaster recovery, and production support.
- Knowledge of DevSecOps practices and cloud/container security.
- Experience deploying AI/ML workloads and model-serving services in containerized environments.
- Familiarity with MLOps tools such as MLflow, Kubeflow, or KServe.
- Understanding of LLM deployment, model inference, GPU-based workloads, and resource optimization is an advantage.
- Exposure to AI-assisted development and operations tools for automation, troubleshooting, and incident management.
- Knowledge of model lifecycle management, model monitoring, and scalable AI service deployment
Salary Range: $110,000-$120,000 a year