Aayush Thakur

Quality Engineering Lead for AI and SaaS Risk-led quality across software, data, and AI

QA Lead for AI-powered search, Copilot and data products, who previously shaped SaaS test strategy that cut production defects by 80% within two consecutive release cycles.

Highlighted stories

Test strategy for a large-scale SaaS platform

A structured plan for release quality

A large-scale SaaS platform needed comprehensive quality planning. Aayush designed and executed a test strategy and plan that reduced production defects by 80% within two consecutive release cycles.

Structures quality around a clear test plan

Draup Technologies

SDET II |

High-volume performance and load testing

Finding bottlenecks under load

Draup Technologies needed high-volume performance and load testing for its application. Aayush’s work improved overall application throughput by 70%.

Finds bottlenecks through performance testing

Draup Technologies

SDET I |

Custom automated test frameworks

Broader coverage across sprint cycles

Regression testing across sprint cycles called for broader automated coverage. Aayush developed custom frameworks that raised test coverage from 60% to 90%.

Builds automation for repeatable coverage

Draup Technologies

SDET II |

Agentic QA workflows

Nightly regression across environments

Nightly regression testing needed to run across multiple environments. Aayush designed automated workflows for zero-touch execution and faster feedback.

Automates repeatable regression work

Draup Technologies

SDET III — QA Lead |

AI-assisted test development

Reducing manual test development work

Manual QA effort remained part of test script and test case development. Aayush used AI-assisted development and test case generation to reduce that effort by 60%.

Applies automation to manual QA work

Draup Technologies

SDET III — QA Lead |

Defect tracking and triage system

Structured defect resolution

Defect resolution and release quality transparency called for a structured tracking and triage system. The system cut bug resolution cycle time by 45%.

Makes defect handling more structured

Draup Technologies

SDET II |

Datadog API monitoring

Monitoring APIs for faster incident response

Real-time incident detection was needed across monitored API environments. Aayush built dashboards and validations that improved detection accuracy and reduced mean-time-to-resolution by 40%.

Uses monitoring to support incident response

Draup Technologies

SDET III — QA Lead |

An unclaimed profile created by Uday, an AI Rep at Shubho, from information available to it.
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