Technical Director at Globant - Pune, India

Designing Quality Engineering platforms that improve release confidence at scale.

I lead enterprise Quality Engineering initiatives focused on scalable automation architecture, AI-enabled engineering workflows, platform thinking, and delivery confidence. My work combines engineering strategy with hands-on execution to help teams build faster, reduce operational friction, and improve release reliability across large-scale programs.

Rajesh Yemul

Current focus

Building scalable QE platforms, improving automation reliability, integrating AI responsibly into engineering workflows, and helping teams adopt reusable quality systems with less operational overhead.

Where I create leverage

  • Transforming QE from a testing function into a platform capability.
  • Building automation ecosystems aligned with business risk and delivery speed.
  • Improving test reliability, execution visibility, and engineering feedback loops.

Public proof points behind the story

Selected highlights from enterprise Quality Engineering leadership, platform engineering, open-source contributions, and scalable automation initiatives.

13+ years in Quality Engineering across retail, e-commerce, automotive, and manufacturing
50+ engineers aligned across multi-program QE portfolios
17+ enterprise initiatives supported across automation modernization, platform engineering, and QE transformation programs
44 public GitHub repositories across frameworks, playbooks, and learning assets
3 published npm packages focused on CI feedback speed and flaky test intelligence

What I am hired to solve

At director level, the work is not only about frameworks. It is about shaping the systems, standards, and teams that make quality sustainable at enterprise scale.

01

Architecture before automation volume

I design modular QE ecosystems with reusable frameworks, engineering standards, observability, and governance models that remain maintainable as organizations scale.

02

AI within engineered guardrails

I use AI where it improves engineering productivity, diagnostics, and delivery visibility while maintaining reliability, traceability, and engineering guardrails.

03

Operating model and executive alignment

I connect quality strategy to release confidence, business risk, and delivery timelines so QE becomes an enabler of velocity, not a reactive checkpoint.

04

Capability building with a teacher's mindset

I care deeply about uplift. My goal is to leave behind teams that think architecturally, own their systems, and no longer depend on heroic intervention.

How I think about modern Quality Engineering

The frameworks matter, but the philosophy matters more. These principles guide how I design systems, coach teams, and evaluate whether quality work is actually creating leverage.

Quality is a system property

Quality should be built into engineering systems, not added at the end.

Signal beats test count

Reliable insight is more valuable than large suites that teams no longer trust.

AI must earn its place

It belongs where it reduces cognitive load, improves decision quality, and respects guardrails.

Leadership should remove dependency

The goal is to build teams and platforms that work well without constant escalation.

Open to conversations about QE transformation, platform strategy, and AI-enabled automation.

I enjoy connecting with engineering leaders, architects, and teams working on automation modernization, QE transformation, developer productivity, and AI-enabled engineering workflows.