Cloud Engineer

Responsibilities

• • Cloud Infrastructure Design: Architect and deploy scalable, secure, and cost-efficient cloud infrastructure using platforms like AWS, Google Cloud, or Azure.
  • System Deployment: Deploy and manage AI models, applications, and services in cloud environments, ensuring seamless integration and performance.
  • Automation: Develop and maintain infrastructure-as-code (IaC) using tools like Terraform, CloudFormation, or Ansible to automate provisioning and management.
  • CI/CD Pipelines: Build and optimize continuous integration and continuous deployment (CI/CD) pipelines to support rapid development and deployment cycles.
  • Monitoring & Optimization: Implement monitoring, logging, and alerting systems (e.g., Prometheus, Grafana, CloudWatch) to ensure system reliability and performance optimization.
  • Security & Compliance: Ensure cloud infrastructure adheres to security best practices and compliance requirements, implementing measures like encryption, IAM policies, and network security.
  • Scalability: Design systems to handle high-volume workloads, optimizing for cost, performance, and scalability to support AI-driven applications.
  • Collaboration: Work closely with data scientists and software engineers to integrate machine learning models and applications into cloud environments.
  • Troubleshooting: Diagnose and resolve infrastructure issues, ensuring minimal downtime and optimal system performance.
  • Documentation: Maintain clear documentation of cloud architecture, configurations, and processes to support team collaboration and knowledge sharing.

Must Have

• • Education: Bachelor’s degree in Computer Science, Information Technology, or a related field. Advanced certifications (e.g., AWS Certified Solutions Architect, Google Cloud Professional Engineer) are a plus.
  • Experience: 3+ years of experience in cloud engineering, DevOps, or a related role, preferably in a tech or AI-focused environment.

• Expertise in at least one major cloud platform (AWS, Google Cloud, Azure).
• Proficiency with infrastructure-as-code tools (e.g., Terraform, CloudFormation).
• Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Strong scripting skills in languages like Python, Bash, or PowerShell.
• Familiarity with CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions).
• Knowledge of networking, security, and database management in cloud environments.
• Ability to work in a fast-paced startup environment with a focus on collaboration and problem-solving.

Nice to have

• Experience supporting AI/ML workloads in the cloud (e.g., model training, inference).
• Familiarity with MLOps tools and practices for deploying machine learning models.
• Knowledge of serverless architectures or big data technologies (e.g., Spark, Hadoop).
• Contributions to open-source projects or relevant certification