Cloud Operations Intern

Overview

We are looking for a motivated, detail-oriented, and technically curious Cloud Operations(SRE) Intern to join our CloudOps team. The intern will gain hands-on experience in day-to-day cloud operations, monitoring and incident triage, infrastructure automation and CI/CD, cloud cost management (FinOps), security and compliance, and AI-assisted operations while collaborating with experienced cloud engineers, and application development teams.

Our intent is to develop an engineer who can understand the platform end to end, connect application and business needs to reliable, secure, and cost-efficient cloud operations, evaluate state-of-the-art automation approaches, and contribute to the implementation of intelligent and agentic operations solutions.

Responsibilities

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.

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.

Qualifications

Required Skills

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.

Nice to have skills

Hands-on exposure to AWS, and ideally Azure, through coursework, labs, or personal projects; entry-level certifications such as AWS Certified Cloud Practitioner, AWS Certified AI Practitioner, or Microsoft Certified: Azure Fundamentals (AZ-900) are a plus.

Exposure