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Senior Physical Design Engineer @ AMD | Advanced‑Node CPU Tiles | Timing Closure | AI for EDA
Physical design at advanced nodes is not just implementation — it is problem‑solving under pressure. A single placement decision or missed timing path can cost weeks of debug. That reality is what drew me to this field and continues to guide how I work. I am a Senior Physical Design Engineer at AMD with 5.5 years of VLSI experience, working on complex CPU tile implementations at leading‑edge process nodes. My work spans floorplanning through signoff, with deep focus on cross‑tile timing closure, routing convergence in highly congested designs, power integrity, and late‑stage ECO cycles where margins are thin and risk is high. I particularly enjoy root‑cause analysis — understanding why a design does not converge, whether that means tracing timing failures back to placement decisions or identifying routing strategies that no longer scale at newer nodes. This mindset is what I consider the difference between implementation and engineering. Alongside hands‑on PD ownership, I develop TCL and Python automation to accelerate report analysis, ECO debug, and DRC triage. I am strongly interested in ML‑assisted EDA and believe the next leap in physical design productivity will come from engineers who understand both silicon physics and machine learning models.
Udacity
Nanodegree, Machine Learning with tensorflow
January 1, 2020 – January 1, 2020
VNR Vignana Jyothi Institute of Engineering and Technology (VNRVJIET)
Bachelor of Technology, Electronics and Communications Engineering
January 1, 2016 – January 1, 2020
sri chaitanya jr kalashala
inter, mpc
January 1, 2014 – January 1, 2016
AMD
Senior Silicon Design Engineer
March 1, 2026 – Present
Hyderabad, Telangana, India · On-site
Laksh Semiconductors
ASIC Physical Design Engineer
November 1, 2022 – March 1, 2026
Hyderabad, Telangana, India · On-site
Tata Consultancy Services
System Engineer
July 1, 2020 – September 1, 2022
Hyderabad, Telangana, India · On-site
Bennett University
Research Intern
May 1, 2020 – June 1, 2020
VNR Vignanajyothi Institute of Engineering & Technology
Research Assistant
January 1, 2020 – June 1, 2020
Chegg Inc.
Subject Matter Expert Q&A Calculus
December 1, 2019 – February 1, 2022
Remote
VNR Vignanajyothi Institute of Engineering & Technology
Teaching Assistant
September 1, 2019 – January 1, 2020
IEEE STUDENT BRANCH - VNRVJIET
Vice Chair
March 1, 2019 – June 1, 2020
Hyderabad, Telangana, India
The Climber
Business Development Intern
April 1, 2018 – May 1, 2018
Student Networking Festival
Visual Designer
April 1, 2018 – July 1, 2018
Hyderabad, Telangana, India
The Climber
Organizing Committee member
March 1, 2018 – March 1, 2018
The Climber
Marketing Manager
February 1, 2018 – September 1, 2018
IEEE STUDENT BRANCH - VNRVJIET
Volunteer
July 1, 2016 – June 1, 2020
Hyderabad, Telangana, India
Purchase amount Prediction
June 1, 2022 – June 1, 2022
This pet-project was my experimentation of using ML Pipeline in PySpark, to understand the usage of ML pipeline in Spark. Worked with Spark ML pipeline, Spark - SQL, Spark Data Frame during this exploration. Created a model to predict the purchase amount of customer against various products which will help them to create personalized offer for customers against different products.
Quantum Circuit Generation
September 1, 2021 – October 1, 2021
Created a function for super-positioning the bits for the given condition. Using Qiskit Library generated the circuit to measure the probabilities at the end of Quantum registers. Plotted the histogram of probabilities resulted after using the QASM Simulator on the circuit.
Image Classifier
March 1, 2020 – April 1, 2020
This was one of the graded projects during the course of Nanodegree Programme of Udacity. This project has been a great way to enhance my skills in creating models in deep learning using Keras, TensorFlow, and how to implement transfer learning. During this project, I also learned how to save models and load models which essentially helped me to understand the pipeline of deployment of a model. I intend to deploy this model later using Heroku as well, after learning it.
Breast Cancer Detetction
October 1, 2019 – March 1, 2020
Classifying whether the patients are suffering from breast cancer or not. The first stage of this project involved classifying patients based on the features using the Wiscon Breast cancer dataset to train the model using the Tensorflow framework. Now working on fine-tuning the created model which is trained on the dataset of images belonging to patients suffering from breast cancer or not, the data is of mammograms. CNN architecture is being used to train the model.
Crop disease detection
March 1, 2019 – April 1, 2019
Implemented Disease type detection for various crops using Transfer learning methodology from the reference of a research paper, which involved the famous CNN architecture named ‘AlexNet’. Used the diseased and healthy crop dataset to train the model to extract the featured on the MATLAB platform, and achieved an accuracy of 98.7%.
Smart Immersion Water Heater
September 1, 2017 – March 1, 2018
The project is an advanced version of the traditional immersion rod, which is used for heating the water for different purposes. The prototype built uses various sensors to measure the temperature, to control the temperature and to stop energy wastage which happens in the form of heating of water. Microcontroller ATMEL 328 is the heart of this project, which has been used in this project.
Structuring Machine Learning Projects
Coursera
June 23, 2026 – Present
Introduction to Programming Using Python
Microsoft
June 23, 2026 – Present
Prime Time: Jumpstart
Synopsys Inc
June 23, 2026 – Present
Mastering Apache Spark using Python
Analytics Vidhya
June 23, 2026 – Present
Deep Learning
NPTEL
June 23, 2026 – Present
Fusion Compiler : Jumpstart
Synopsys Inc
June 23, 2026 – Present
AWS Machine Learning
Udacity
June 23, 2026 – Present
Deep learning Professional Certificate
IBM
June 23, 2026 – Present
Building Deep Learning Applications with Keras 2.0
June 23, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 23, 2026 – Present
NVIDIA DLI Certificate - FUNDAMENTALS OF DEEP LEARNING FOR COMPUTER VISION
NVIDIA
June 23, 2026 – Present
Machine learning with tensorflow Nanodegree
Udacity
June 23, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 23, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from ML pipelines to quantum computing and embedded systems, shows a broad intellectual curiosity and a proactive approach to learning. The transition from Silicon Design to Data Analysis indicates adaptability and a drive to pivot careers. While the professional experience is not directly in data analysis, the personal projects and certifications strongly align with the target role, suggesting a good cultural fit for a data-driven, innovative environment.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative through numerous personal projects and a commitment to learning new technologies. Experience as a Teaching Assistant and Vice Chair of IEEE Student Branch suggests good communication and leadership potential. However, the primary professional experience is in Silicon Design Engineering, which is not directly aligned with a Data Analyst role, indicating a potential gap in operational fit for data-specific workflows.