Cloud Operations Internship

Skills this role screens for

About the role

Shure is hiring for the role of Cloud Operations Intern!

The work

Responsibilities of the Intern

• Understand the platform architecture (mainly AWS, with some Azure), the multi-account setup across production and non-production environments, service dependencies, deployment workflows, and the main reliability, security, and cost risks.
• Assist with day-to-day cloud operations, including environment health checks, configuration reviews, access requests, backups, and resource inventory, starting in non-production environments under guidance.
• Build and maintain monitoring for cloud services, including CloudWatch metrics, log-based metric filters, alarms, and dashboards, and help tune thresholds to reduce alert noise.
• Participate in incident investigation and log triage; query logs and metrics with tools such as CloudWatch Logs Insights, reproduce issues, support root-cause analysis and post-incident reviews, and turn findings into runbooks and triage playbooks.
• Write and maintain Infrastructure as Code (AWS CloudFormation, Terraform) and Python or Typescript automation for repeatable operational tasks, following peer review and change-management practices.
• Support Jenkins-based CI/CD pipelines and build infrastructure; analyze pipeline results, build logs, and agent failures, and help keep controllers and agents healthy and current.
• Contribute to FinOps work across AWS and Azure, such as cost reporting and dashboards, tagging hygiene, finding idle or unused resources, rightsizing, and Savings Plans and Reserved Instance coverage reviews.
• Support security and compliance work, including IAM least-privilege reviews, key and secret hygiene, audit evidence collection, and control mapping for SOC 2 and FedRAMP.
• Prototype AI-assisted operations using LLMs, Generative AI, and AI agents for activities such as alarm and ticket triage, log summarization, incident analysis, cost anomaly explanation, and runbook maintenance.
• Contribute to the team’s Amazon Bedrock CloudOps prototype, an agentic AI assistant that plans read-only investigations, queries logs, metrics, and platform data through approved tools, uses the team’s playbooks as a knowledge base, and reports findings, with PII masking, guardrails, and human review built in.
• Measure monitoring and automation effectiveness by tracking alert noise, time to detect and resolve incidents, automation coverage, and cost savings, and recommend practical improvements.
• Learn and apply the AWS Well-Architected Framework, SRE principles such as toil reduction, the FinOps lifecycle, and secure operations practices.

Who they want

Required Qualifications

• Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Information Technology, Computer or Electronics Engineering, Software Engineering, Artificial Intelligence, Data Science, or a related field.
• Basic understanding of cloud computing concepts such as compute, storage, identity and access management, and virtual networks, along with Linux and networking fundamentals (DNS, TCP/IP, HTTP, load balancing).
• Working knowledge of at least one programming or scripting language such as Python, Java, TypeScript.
• Foundational understanding of source control (Git), CI/CD concepts, APIs, JSON and YAML, SQL, and ticketing tools such as Jira.
• Essential exposure to and conceptual understanding of Large Language Models (LLMs), Generative AI, and Agentic AI, including their practical use in software engineering or IT operations.
• Ability to understand a system end to end, troubleshoot methodically from symptom to cause, learn unfamiliar technologies, and communicate findings clearly.
• Strong attention to detail, ownership, curiosity, collaboration skills, and willingness to experiment and learn, with care and discipline around production systems.

Who they want

Requirements

• Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Information Technology, Computer or Electronics Engineering, Software Engineering, Artificial Intelligence, Data Science, or a related field.
• Basic understanding of cloud computing concepts such as compute, storage, identity and access management, and virtual networks, along with Linux and networking fundamentals (DNS, TCP/IP, HTTP, load balancing).
• Working knowledge of at least one programming or scripting language such as Python, Java, TypeScript.
• Foundational understanding of source control (Git), CI/CD concepts, APIs, JSON and YAML, SQL, and ticketing tools such as Jira.
• Essential exposure to and conceptual understanding of Large Language Models (LLMs), Generative AI, and Agentic AI, including their practical use in software engineering or IT operations.
• Ability to understand a system end to end, troubleshoot methodically from symptom to cause, learn unfamiliar technologies, and communicate findings clearly.
• Strong attention to detail, ownership, curiosity, collaboration skills, and willingness to experiment and learn, with care and discipline around production systems.