---
title: "Veeresh Anumandla - AI Automation Engineer in Hyderabad | Tal"
description: "Meet Veeresh Anumandla, Agentic AI Automation Engineer @ Amdocs | Agentic AI + Playwright MCP + Cursor Agent | 8+ yrs SDET | 5G Charging | Building LLM-powered self-healing test frameworks | Open to AI QA Lead roles based in Hyderabad. AI stack includes claude and cursor."
image: "https://tal.club/meet/anumandla-veeresh/share-image"
---

# Veeresh Anumandla on Tal

## Headline

Agentic AI Automation Engineer @ Amdocs \| Agentic AI \+ Playwright MCP \+ Cursor Agent \| 8\+ yrs SDET \| 5G Charging \| Building LLM\-powered self\-healing test frameworks \| Open to AI QA Lead roles

## About

I'm an Agentic AI Automation Engineer with 8\+ years turning manual test workflows into intelligent, self\-healing systems\. Today I sit at the intersection of Test Automation and Agentic AI at Amdocs, on AT&T's 5G Charging & Billing platform — using LLMs \(Cursor Agent, OpenAI GPT\-4/4o, Anthropic Claude\) not as a shiny add\-on, but as an everyday engineering companion for every part of the loop: understanding requirements, designing tests, generating code, debugging failures, and shipping automation for teammates on the same day they ask for it\. What I've built recently: • Code\-less, prompt\-driven UI automation tool — Cursor Agent \+ Playwright \+ TypeScript \+ Model Context Protocol \(MCP\), with LLM\-powered auto\-healing locators\. Teammates describe a flow in plain English; the tool records, replays, self\-repairs on UI changes, and caches learned selectors so the next run is 30× faster\. • RAG knowledge base of test runbooks, reference data, and environment topology — so AI\-generated test steps are context\-aware and environment\-correct instead of hallucinated\. • Reusable prompt library \(few\-shot, chain\-of\-thought, role\-based personas\) that enforces consistent, hard\-assertion\-first test output across the whole team\. • Self\-service Test Execution Dashboard — a web UI that lets manual testers launch, monitor, and get results from automated suites without touching an IDE\. Removed the "automation engineer bottleneck" for the whole team\. • Agentic flows that orchestrate multi\-step scenarios end\-to\-end — environment discovery → provisioning → usage → downstream verification — with minimal human intervention\. Numbers I'm proud of: 50%\+ reduction in test design effort per release, 60\+ high\-impact defects caught in a single feature \(earned a performance bonus\), and regression cycles compressed to a fraction of what they were pre\-AI\. Underneath the AI, the foundation is solid: Java 8/11/17, Python, TypeScript, Selenium, Playwright, REST Assured, TestNG, Jenkins, Docker, Kubernetes, Kafka\. I move comfortably between designing an AI\-augmented framework and getting hands\-on with a pod log or a Kafka topic dump at 2 AM\. What I'm looking for: AI\-Powered Test Automation Lead / Architect roles where I can scale this playbook — bring Agentic AI, LLM, and self\-healing automation to a larger org, mentor engineers to work AI\-first, and own quality end\-to\-end for AI\-critical products\. Hyderabad / Pune / Remote\. Let's talk if you're building AI\-first QA teams, hiring for Agentic AI \+ Playwright \+ MCP, or curious how LLMs actually earn their keep in a live production test suite\.

## Location

Hyderabad

## AI stack

claude

cursor

## Work experience

AI Automation Engineer at Amdocs Dvci Private Limited\.,Pune, Nov 2022 \- Present · 3 yrs 9 mos, Pune District, Client: AT&T Charging & Billing Platform \(5G, Telecom\) • Architected an Agentic AI test automation platform using Cursor Agent, OpenAI GPT\-4/4o, and Anthropic Claude — LLMs translate design specs and API schemas directly into runnable automation, cutting per\-release test design effort by 50%\+\. • Built a code\-less, prompt\-driven UI automation tool from scratch: Playwright \+ TypeScript \+ Model Context Protocol \(MCP\) \+ LLM auto\-healing locators\. Teammates describe UI flows in plain English; the tool records, replays, self\-heals on DOM changes, and caches learned selectors — reducing UI automation authoring time from hours to minutes\. • Designed a Retrieval\-Augmented Generation \(RAG\) knowledge base of test runbooks, reference data, and environment topology — enables context\-aware, environment\-correct AI\-generated test steps and self\-service debugging\. • Authored a reusable Prompt Engineering library \(few\-shot, chain\-of\-thought, role\-based personas\) that standardizes AI output across activation, usage, and billing test flows\. • Delivered a Self\-Service Test Execution Dashboard — web UI that lets manual testers launch and monitor automated test runs without an IDE\. • Orchestrated agentic multi\-step flows end\-to\-end: environment discovery → subscriber provisioning → usage consumption → downstream Kafka/record verification — with minimal human intervention\. • Built REST API automation for 5G charging microservices deployed on Kubernetes; integrated AI\-augmented suites into Jenkins CI/CD with Docker\. • Performed defect triage and RCA using Splunk, AppDynamics, and pod\-level logs; drove issues to closure in JIRA\. Stack: Cursor Agent, Playwright MCP, TypeScript, Java 17, Python, OpenAI GPT\-4/4o, Anthropic Claude, RAG, Selenium, REST Assured, TestNG, Jenkins, Docker, Kubernetes, Kafka, Splunk, AppDynamics\.

[View the original profile](https://tal.club/meet/anumandla-veeresh)

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