Senior Devops Engineer
Summary
Senior DevOps engineer in Hyderabad (on-site) who builds and maintains cloud infrastructure automation, CI/CD pipelines, and Kubernetes environments, with responsibilities extending to supporting AI platform operations and model deployment. Core stack: AWS/Azure/GCP, Docker/Kubernetes, Terraform, and Python/Bash scripting.
JD:
Senior DevOps Engineer
Must-Have Technical Skills
- Strong experience in DevOps engineering and infrastructure development.
- Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).
- Strong knowledge of Docker and Kubernetes.
- Experience in CI/CD tools such as Jenkins, GitLab CI, GitHub Actions, or Azure DevOps.
- Expertise in Infrastructure as Code (Terraform, Ansible, or CloudFormation).
- Strong scripting and automation skills using Python, Bash, or Shell.
- Experience with Linux administration and system troubleshooting.
- Knowledge of networking concepts, load balancing, DNS, firewalls, and security.
- Experience with monitoring and observability tools such as Prometheus, Grafana, ELK, or Datadog.
- Understanding of cloud security, IAM, secrets management, and access controls.
- Experience in production support, incident management, and root cause analysis.
Good-to-Have Skills
- Exposure to AI/ML platform infrastructure and operations.
- Knowledge of MLOps and LLMOps practices.
- Experience with GPU-based infrastructure and accelerated computing.
- Understanding of model serving frameworks such as NVIDIA Triton, KServe, or MLflow.
- Exposure to AI frameworks and platforms such as TensorFlow, PyTorch, or Hugging Face.
- Knowledge of workflow orchestration tools such as Airflow or Kubeflow.
- Experience with scalable distributed systems and microservices architecture.
Roles and Responsibilities
- Develop and maintain infrastructure automation solutions.
- Manage cloud-native infrastructure and Kubernetes environments.
- Implement automated deployment and release management processes.
- Support AI platform operations and model deployment workflows.
- Ensure infrastructure reliability, availability, and scalability.
- Identify and resolve performance bottlenecks and operational issues.
- Maintain infrastructure documentation and operational procedures.
- Work with cross-functional teams to deliver reliable AI platform solutions.
Behavioral Competencies
- Strong analytical and problem-solving skills.
- Excellent communication and collaboration abilities.
- Ability to work independently and manage multiple priorities.
- Strong ownership and accountability.
- Ability to work in Agile and DevOps environments.
- Continuous learning mindset and adaptability to emerging technologies.
Skills
- Agile
- AI
- Airflow
- Ansible
- Automation
- AWS
- Azure
- Azure DevOps
- Bash
- CI/CD
- Cloud
- Cloud Native
- Cloud Security
- CloudFormation
- Datadog
- DevOps
- Distributed Systems
- DNS
- Docker
- ELK
- Firewall
- GCP
- GitHub
- GitHub Actions
- GitLab
- GPU
- Grafana
- Hugging Face
- IAM
- Infrastructure as Code
- Jenkins
- Kubeflow
- Kubernetes
- Linux
- LLMOps
- Machine Learning
- Microservices
- MLflow
- MLOps
- Model Deployment
- Networking
- Observability
- Prometheus
- Python
- PyTorch
- Root Cause Analysis
- Secrets Management
- TensorFlow
- Terraform
- Triton
- Workflow Orchestration