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Karthik Kodati, home

About

Quality engineering, treated as engineering.

I'm a quality engineering professional with 9+ years of experience designing, building, and leading automation across enterprise applications, financial systems, public-sector systems, and mobile platforms.
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Location
Hyderabad, India
Experience
9+ years
Focus
Test architecture, AI-augmented QE

My work is building automation frameworks, not only writing automated tests. That means architecture decisions: how a framework handles configuration across countries and tenants, how UI and API evidence fit together, how suites run in parallel inside CI, and how a team extends the framework without breaking it.

At Zensar Technologies I architected a Playwright and TypeScript framework for global Workday HCM, Payroll, Finance, and Integrations. I have also led QA on financial systems built on Kafka event streams, led a test automation transformation, owned mobile release quality on Android and iOS, and progressed through engineering and QA leadership roles.

My current direction is AI-augmented quality engineering: combining deterministic automation with responsible AI for test generation, API testing, and engineering tooling. I treat that work as engineering, with the same expectations for review, evidence, and accountability.

Approach

How I approach a quality problem.

  1. Start from risk

    Decide what has to be true for a release to be safe, then design coverage around it.

  2. Architect for the next engineer

    Clear layers, typed interfaces, and conventions that make the framework easy to extend.

  3. Make failures cheap to diagnose

    Traces, logs, and API evidence attached to every failure, so debugging starts with facts.

  4. Keep the signal honest

    Flaky tests are defects. A quality gate is only useful when the team trusts it.

Principles

What I hold to.

  • Automate for measurable value

    Coverage is chosen for the risk it retires, not for the number of tests it adds.

  • Test frameworks are production software

    They get architecture, code review, type safety, and maintenance like any other system.

  • Reliability starts in CI/CD

    Quality signals are designed into the pipeline from the first commit, not bolted on before release.

  • Observable over clever

    Maintainable, debuggable systems are preferred over shortcuts that are hard to diagnose later.

  • AI amplifies judgment

    AI tooling supports engineering decisions. It does not replace the engineer who is accountable for them.

Toolkit

Technologies I work with.

Automation
Playwright · TypeScript · JavaScript · Selenium · Mobile automation (Android, iOS)
APIs and integration
REST APIs · SOAP integrations · Workday Integrations · Kafka
Delivery and quality signals
GitHub · CI/CD · SonarQube · Splunk · Dynatrace
Enterprise domains
Workday HCM · Payroll · Finance · Financial services · Public sector
Current direction
AI-assisted test generation · API testing workflows · Engineering tooling
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