JK_DEVOPS
DevOps & Cloud Engineer — Banking & Enterprise Platforms

Jeevan
Katta

I build and harden the pipelines that move code and data safely through banking-grade systems — AWS, Kubernetes, Terraform, and increasingly, AI-assisted engineering workflows.

AWSKubernetesTerraform DockerJenkins / GitHub Actions GitHub CopilotAmazon Q
See the work → Get in touch

Delivery pipeline

01Code+ Copilot
02Build+ Amazon Q
03Pipelineguardrails
04AWSdeploy
05AlertSOP
01 / About

Summary

Who I am

Software Engineer / Application Analyst at Cognizant, supporting Truist Bank on DevOps and cloud engineering. I work at the intersection of infrastructure reliability and AI-assisted delivery — piloting tools like GitHub Copilot and Amazon Q inside a regulated banking environment, then proving their value with working systems rather than slide decks.

Core stack
AWS (EC2, VPC)KubernetesTerraform DockerJenkinsGitHub Actions DynatraceCloudWatchSplunk Oracle SQLShell ScriptingJava / Spring Boot
02 / Projects

Selected work

01

Banking Platform — AI-Assisted Development Pilot

Designed and built a bank management system on Java Spring Boot microservices, then used it as the live proof-of-concept to win client approval for GitHub Copilot and Amazon Q inside the delivery workflow.

X — RESULT

Client sign-off to adopt AI coding assistants project-wide

Y — MEASURED BY

A working demo the client evaluated directly

Z — METHOD

Built full microservices system with Copilot + Amazon Q in the loop

JavaSpring BootMicroservices GitHub CopilotAmazon Q
02

Java 8 → Java 17 Migration, AI-Accelerated

Modernized a legacy Java 8 codebase to Java 17, using AI coding assistants to accelerate refactoring of deprecated APIs, syntax updates, and dependency upgrades.

X — RESULT

Codebase upgraded to a current, supported Java version

Y — MEASURED BY

Migration completed with AI-assisted refactor cycles

Z — METHOD

Copilot / Amazon Q-assisted refactoring across the service

Java 17Spring Boot GitHub CopilotAmazon Q
03

Incident AI Playbook

Built and trained an AI-driven playbook on historical incident and failure data. On a new issue, it outputs exact SOP-style troubleshooting steps and routes the ticket to the owning team automatically.

X — RESULT

Faster, more consistent incident triage

Y — MEASURED BY

Step-by-step SOP output per incident type

Z — METHOD

Trained the playbook on past failure/issue history

AI/MLIncident Management AutomationSOP Design
04

ETL Pipeline Failure Guardrails

Wrote shell scripts that catch conditions leading to extract/load failures in production data pipelines before they cause an outage, rather than reacting after the fact.

X — RESULT

Reduced recurring extract/load pipeline failures

Y — MEASURED BY

Pre-failure checks blocking bad runs

Z — METHOD

Custom shell scripting embedded in the pipeline

Shell ScriptingETLPipeline Reliability
05

Oracle → AWS Data Migration

Migrated the full estate of Oracle SQL tables to AWS as part of the platform's cloud transition.

X — RESULT

Full Oracle table estate running on AWS

Y — MEASURED BY

Complete migration with no legacy Oracle dependency remaining

Z — METHOD

Planned and executed schema + data migration to AWS

Oracle SQLAWSData Migration
03 / Certifications

Credentials

AWS

AWS Certified Cloud Practitioner

Amazon Web Services

04 / Contact

Say hello

Let's build something reliable.

Hyderabad, India — DevOps & Cloud Engineering

jeevankatta17@gmail.com LinkedIn GitHub