Pavan Teja Tiwari (Pavan) Gadiraju

Hyderabad

Summary

Personal statement: Pavan is a talented and seasoned engineer with 2 years of IT experience, currently working in Tata Consulting Services (TCS). I have passion to learn on new technologies, especially in Data Science, AI, ML and Cloud. An excellent team player and focuses on Project and Company/client success.

Overview

years of professional experience

years of post-secondary education

Work History

System Engineer

Tata Consulting Services, British Telecom

09.2020 - Current

Application - Consumer and business hubs
Key Project responsibilities:
Automation Development with python on all Customer premises equipment( CPE) scripts for consumer and business side hubs
Analysing failures when occurs during testing.
Preprocessing and cleaning the the data from logs.
Manipulating the data in SQL
Applying statistics to find the data insights.
Applying ML model for redictions.
Deploying the model in GCP.

Education

PGD - Artificial intelligence Intelligence Machine

NIT

12.2020 - 06.2022

B.Tech - Electronics and Communication Engineering

Prasad V Potluri Siddhartha Institute of Technology

06.2016 - 04.2020

Skills

SQL- Have Knowledge in SQL and currently learning advanced SQLundefined

Accomplishments

2020 - 2 times Special Initiation award
2020 - Elevate wings track completion award
2020 - Digital Cadre in TCS
2021 - Elevate wings track completion award
2019 – National Yoga Championship
2016- Achieved NCC – C Certificate

Personal projects

Recommendation System

Designing of a system which recommends a product based on customer interest
Built using count vectorisation in ”scikit-learn”for extraction of similarity matrix
This system fits good forlarge user data for quality outcomes hence can be empowered by large data collection

Hand written character recognition by deep learning:

Building a model which identifies the digits from a Hand written dataset
Developed a multi perceptron model in Deep learning using Tensorflow 2.0
Model Accuracy can be improved by using more epoch and good optimiser, In this ’NADAM’ optimiseris taken and acheived 99.5 percent accuracy

Timeline