Job Type: Full-Time
Exp: 4 Years+
Location: Bangalore
Job Overview:
We are looking for an experienced DevOps Engineer who is well-versed in managing cloud infrastructure, containerization, and the deployment of machine learning (ML) and artificial intelligence (AI) applications, as well as traditional web applications. You will be responsible for automating and optimizing cloud-based environments, ensuring continuous integration/continuous delivery (CI/CD) pipelines, and supporting a scalable infrastructure for various teams working on web, ML, and AI solutions.
Key Responsibilities:
Cloud Infrastructure Management:
Design, deploy, and manage virtual machines (VMs), Kubernetes clusters, and cloud infrastructure on platforms such as AWS and DigitalOcean.
Ensure high availability, security, and scalability of applications hosted in the cloud.
Containerization & Orchestration:
Set up, configure, and manage Kubernetes clusters for container orchestration.
Automate deployment pipelines using tools like Jenkins, GitLab CI, or CircleCI.
CI/CD Pipelines:
Implement CI/CD pipelines for ML/AI and web applications to ensure automated testing, integration, and deployment across multiple environments.
Collaborate with development and data science teams to streamline the release process.
ML/AI Application Deployment:
Deploy machine learning and AI models into production environments.
Manage model lifecycle including model retraining, versioning, and scaling.
Web Application Deployment:
Deploy, monitor, and maintain web applications using cloud services and containerization tools.
Monitoring & Logging:
Set up monitoring, logging, and alerting for all services and applications using tools like Prometheus, Grafana, ELK stack, or AWS CloudWatch.
Automation & Scripting:
Develop scripts and automation tools to simplify day-to-day operations and manage infrastructure as code (IaC) using tools like Terraform, Ansible, or AWS CloudFormation.
Security & Compliance:
Implement cloud security best practices and compliance standards.
Ensure that systems are hardened and vulnerabilities are addressed promptly.
Collaboration & Support:
Work closely with development, AI, and data science teams to ensure seamless integration of their work into the cloud environment.
Provide technical support and troubleshoot issues related to infrastructure and deployments.
Required Skills:
Strong experience with Kubernetes and containerization (Docker).
Hands-on experience with AWS and DigitalOcean cloud services.
Proficiency in deploying ML/AI models in production using cloud-based or containerized environments.
Familiarity with VM management and cloud-based virtual machine deployments.
Experience with CI/CD tools like Jenkins, GitLab CI, CircleCI, etc.
Proficiency in scripting languages such as Python, Bash, or Shell scripting.
Strong knowledge of Infrastructure as Code (IaC) tools like Terraform or AWS CloudFormation.
Experience with monitoring and alerting tools (Prometheus, Grafana, CloudWatch).
Strong understanding of cloud security best practices.
Preferred Qualifications:
Knowledge of Machine Learning and AI pipelines such as TensorFlow, PyTorch, or others.
Experience with serverless architecture (AWS Lambda, etc.).
Certification in AWS (e.g., AWS Certified DevOps Engineer).
Familiarity with networking in cloud environments.