DevOps Engineer Intern - Cloud & AI ML Infrastructure

DevOps Engineer Intern - Cloud & ML Infrastructure

About the Role

We're seeking an experienced DevOps Engineer to design, implement, and maintain our cloud infrastructure across Azure and GCP platforms, with a focus on ML operations and observability. This role combines traditional DevOps responsibilities with cutting-edge LLM deployment expertise.

About the Easework AI

https://www.easeworkai.com

https://www.linkedin.com/in/ratharamanan/

https://www.linkedin.com/company/easeworkai/?viewAsMember=true

https://youtu.be/8h9Qw08kyME?si=Hnj8A7qmwgn2xqak

What we're building:

• AI solutions crafted with real procurement experience
• Smart automation that reflects actual procurement workflows
• Connected Data that provides recommendations for strategy decision making

Our mission is to revolutionize procurement by combining practitioner expertise with AI capabilities, delivering:
✓ Solutions that speak procurement teams' language
✓ Technology that fits real-world workflows
✓ Measurable business impact from day one

Key Responsibilities

CI/CD & Automation

• Design and maintain CI/CD pipelines using industry-standard tools
• Implement automated testing, deployment, and rollback strategies
• Create and maintain Infrastructure as Code (IaC) using tools like Terraform
• Automate routine operational tasks and maintenance procedures

Cloud Infrastructure Management

• Manage multi-cloud infrastructure across Azure and GCP
• Configure and optimize virtual networks, load balancers, and VMs
• Implement and maintain robust security practices across cloud services
• Monitor and optimize cloud resource utilization and costs

Container & Kubernetes Operations

• Design and maintain container orchestration using Kubernetes
• Create and optimize Docker images for various services
• Manage container registries and implement container security practices
• Configure and maintain Kubernetes clusters across cloud providers

ML Operations & Observability

• Implement monitoring and observability solutions for LLM deployments
• Set up logging, metrics collection, and alerting systems
• Monitor model performance and infrastructure health
• Optimize ML inference and training infrastructure

Security & Compliance

• Implement security best practices across all infrastructure
• Maintain compliance with industry standards
• Manage access controls and authentication systems
• Conduct security audits and implement improvements

Required Qualifications

• DevOps or Site Reliability Engineering
• Azure and GCP cloud platforms
• Proficiency in Kubernetes and container orchestration
• Experience with CI/CD tools and practices
• Strong coding skills in Python and shell scripting
• Experience with Infrastructure as Code (Terraform, ARM templates)

Preferred Qualifications

• Experience with ML model deployment and MLOps
• Familiarity with LLM infrastructure and observability
• Cloud certifications (Azure, GCP)
• Experience with monitoring tools (Prometheus, Grafana)
• Knowledge of service mesh technologies (Istio)

Technical Skills

• Cloud Platforms: Azure, GCP
• Containerization: Docker, Kubernetes
• CI/CD: Jenkins, GitLab CI, GitHub Actions
• IaC: Terraform, ARM templates
• Monitoring: Prometheus, Grafana, ELK Stack
• Networking: Load balancing, Virtual Networks, DNS
• Security: IAM, Secret Management, Security Groups
• Languages: Python, Bash, YAML

About Our Team

We're a collaborative, highly innovative team focused on building robust, scalable infrastructure for AI ML applications. We value continuous learning, knowledge sharing, and maintaining high standards in our infrastructure and processes.

Benefits

• Intern convered to Full time role in 3-6 months
• Remote work options
• Learn & Practice cutting edge tech
• Innovate , Fail, Learn, Success & Iterate
• Build word class AI platform from scratch