~/suchitha Resume
Open to DevOps / Cloud / AI Engineering roles

Suchitha Mothkuri

DevOps Engineer

B.Tech graduate building automated, reliable cloud infrastructure on AWS — and the AI agents that run on top of it. I ship CI/CD pipelines, automate the cloud with Python, containerise workloads with Docker and Kubernetes, and provision everything as code with Terraform and Ansible.

Nizamabad, Telangana, India suchithagoudm@gmail.com
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01 — About

From academics to automation

How I built real DevOps and AI engineering capability without a conventional first job.

I am a B.Tech graduate and an aspiring DevOps Engineer with strong, hands-on knowledge of Amazon Web Services and a working understanding of Azure and Google Cloud. I build CI/CD pipelines, containerise applications with Docker and Kubernetes, and provision infrastructure as code using Terraform and Ansible.

Following graduation I took a planned break for personal and family reasons. Rather than let that time go idle, I used it to convert an academic background into practical capability — running self-directed lab projects on real cloud accounts, breaking things on purpose, and learning how to bring them back up.

More recently I have extended that foundation into AI and Agentic AI engineering: building retrieval-augmented pipelines, tool-calling agents, and the container and CI/CD plumbing that puts them into production reliably.

Cloud-first, AWS-primary

EC2, VPC, IAM, S3, ALB, Auto Scaling, RDS, Lambda, ECR and EKS — provisioned by hand first, then as code.

Automate the repeatable

If a deploy takes thirty manual minutes, it belongs in a pipeline. Jenkins, GitHub Actions and Ansible do the boring parts.

AI on solid infrastructure

LLM agents are only as reliable as what they run on — so I treat prompts, models and tools as versioned, monitored releases.

Measure before you claim

Prometheus, Grafana and CloudWatch on every lab build, because "it works" is not the same as "it is healthy".

02 — Skills

The toolchain

Grouped by where it sits in the delivery lifecycle. Levels reflect hands-on lab depth, not years on a payroll.

AWS Compute & NetworkAdvanced

EC2 · VPC · Subnets · Security Groups · ELB/ALB · Auto Scaling · Route 53

AWS Storage & DataAdvanced

S3 · EBS · RDS (MySQL, Multi-AZ) · lifecycle rules

AWS Identity & OpsAdvanced

IAM roles & policies · CloudTrail · Systems Manager · Cost Explorer

AWS Serverless & RegistryIntermediate

Lambda · SNS · ECR · EKS

Microsoft AzureWorking

Virtual Machines · Blob Storage · Azure DevOps Pipelines · AKS basics

Google Cloud PlatformWorking

Compute Engine · Cloud Storage · GKE basics

Delivery pipeline — how a commit reaches production

⎇
Source
⚙
Build & Test
◉
Quality Gate
◳
Image & Push
⬡
Provision (IaC)
⇪
Deploy
✓
Verify
03 — Projects

Things I built and broke

Self-directed lab projects, each run on live cloud accounts rather than a sandbox simulator. Code, Terraform configurations and pipeline definitions are published on GitHub.

project/01 CI/CD

End-to-End CI/CD Pipeline for a Java Web Application

  • Declarative Jenkins pipeline that pulls from GitHub on every commit, compiles with Maven, runs unit tests and publishes the build artifact.
  • SonarQube static analysis wired in as a quality gate that fails the build on critical issues.
  • Application packaged as a Docker image, pushed to Amazon ECR, then deployed to EC2 via an Ansible playbook.
Cut a ~30 minute manual build-and-deploy routine to a single automated run under 5 minutes.
JenkinsMavenSonarQube DockerAnsibleAWS EC2ECR
project/02 Infrastructure as Code

Highly Available Three-Tier Architecture on AWS using Terraform

  • Provisioned a complete three-tier environment as code: custom VPC with public and private subnets across two Availability Zones, NAT Gateway, route tables and security groups.
  • Web servers behind an Application Load Balancer inside an Auto Scaling group, with Amazon RDS (MySQL) Multi-AZ in the private tier.
  • Reusable Terraform modules with remote state in S3 and DynamoDB state locking for safe, repeatable deployments.
Least-privilege IAM roles and instance profiles throughout — zero hard-coded credentials.
TerraformAWS VPCALB Auto ScalingRDSS3DynamoDB
project/03 Kubernetes

Microservices Deployment on Amazon EKS

  • Containerised a multi-service application and deployed it to an Amazon EKS cluster using Deployments, Services, Ingress, ConfigMaps and Secrets.
  • Horizontal Pod Autoscaling plus resource requests and limits to handle variable load, validated under load tests.
  • GitHub Actions workflow that builds images, pushes to ECR and performs a rolling update with automatic rollback on failure.
  • Environment-specific configuration packaged as Helm charts for dev and staging.
Rolling updates with automated rollback — failed releases never reach steady state.
DockerKubernetesEKS HelmGitHub ActionsECR
project/04 Observability

Monitoring, Logging and Alerting Stack

  • Prometheus with Node Exporter collecting infrastructure and container metrics, surfaced through Grafana dashboards for CPU, memory, disk and pod health.
  • CloudWatch alarms and SNS email notifications for CPU thresholds, failed health checks and unusual billing activity.
  • Centralised application logs on the ELK stack to speed up root-cause analysis during deliberate failure simulations.
Failure drills resolved from dashboards and logs rather than guesswork.
PrometheusGrafanaCloudWatch SNSELK
project/05 Automation & FinOps

Multi-Cloud Storage & Cloud Cost Automation

  • Python and Bash automation backing up application data to Amazon S3 with lifecycle rules, replicated to Azure Blob Storage and Google Cloud Storage to compare services.
  • Weekly AWS cost and idle-resource report built on boto3 and Cost Explorer, flagging unattached EBS volumes and stopped instances for cleanup.
  • Jobs scheduled with cron and Lambda, with failure alerts delivered through SNS.
Turned cost review from an ad-hoc chore into a scheduled, alerting report.
Pythonboto3AWS S3 LambdaAzure BlobGCSCost Explorer
project/06 Agentic AI

Agentic AI Assistant for DevOps Workflows

  • Built a tool-calling LLM agent that answers infrastructure questions by querying read-only AWS APIs, Prometheus and pipeline logs through typed tool schemas.
  • Retrieval-augmented layer over runbooks and Terraform documentation so answers cite the source rather than improvising.
  • Scoped every tool to least-privilege IAM, with human-in-the-loop confirmation before any state-changing action.
  • Containerised the service and shipped it through the same GitHub Actions pipeline as the rest of the stack, with latency and token-cost dashboards.
Treats prompts and models as versioned, monitored releases — not configuration drift.
PythonLLM APIsRAG Vector DBFastAPIDockerGitHub Actions

More on GitHub

Project code, Terraform configurations and pipeline definitions are published openly.

github.com/Suchitha123
04 — Journey

Career break & continuous learning

An honest account of the gap, and exactly what went into it.

2021

B.Tech graduation

Completed B.Tech at Bhoj Reddy Engineering College for Women, Hyderabad, with academic project work in power systems and grid-integrated PV control.

2021 — 2026

Planned career break

Stepped away from full-time employment after graduation for personal and family reasons. A deliberate pause, not a drift.

During the break

Structured self-study into DevOps

Moved from an academic background into DevOps through structured self-study and hands-on practice on live AWS, Azure and GCP accounts — paying for and operating real infrastructure rather than reading about it.

Recent

Extended into AI & Agentic AI engineering

Added LLM integration, retrieval-augmented pipelines and tool-calling agent systems on top of the existing cloud and container foundation, treating AI services as production workloads with the same CI/CD and observability discipline.

Now

Seeking an entry-level DevOps / Cloud / AI Engineering role

Preparing for the AWS Certified Solutions Architect – Associate exam, publishing project code on GitHub, and looking to contribute to automation, deployment and reliability work on a real team.

05 — Education

Academics & certifications

Education

B.Tech

Bhoj Reddy Engineering College for Women, Hyderabad 2021 65%

Intermediate

SR Junior College, Nizamabad 2017 88.4%

SSC

ZPHS Yanampalli, Nizamabad 2015 8.3 GPA

Academic projects

Sensorless DC-link control for a three-phase grid-integrated PV system
HVDC tie-link modelling for AGC / LFC in multi-area power systems

Certifications & workshops

AWS Certified Solutions Architect – Associate

currently preparing

Workshop — Solar Panels

completed

Workshop — Advanced Protection Systems of 132/33 KV Substation

completed

Strengths

Hardworking and dedicated · Strong team player · Quick, self-driven learner · Clear communicator · Confident under pressure

06 — Contact

Let's talk

Open to entry-level DevOps, Cloud and AI Engineering roles. The fastest route is email or LinkedIn.

Email suchithagoudm@gmail.com LinkedIn suchitha-mothkuri GitHub Suchitha123
Location Nizamabad, Telangana, India

Ready to contribute from day one

Grab the resume for the full detail, or message me on LinkedIn and I will reply quickly.

Email Suchitha Download Resume