Machine Learning/ Deep Learning Developer at Dell EMC
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• Masters Degree Majors: Artificial Intelligence and Databases • Interested in Deep Learning, Machine Learning and Data Science • Advanced expertise in Python, PL/SQL and SQL
The University of Texas at Arlington
Master of Science (M.S.), Computer Science
January 1, 2015 – January 1, 2017
Visvesvaraya Technological University
Bachelor of Engineering, Information Science and Engineering
January 1, 2007 – January 1, 2011
Dell EMC
Machine Learning/Deep Learning Developer
February 1, 2018 – Present
Austin, Texas
Dell
Software Dev. Analyst
May 1, 2012 – July 1, 2015
Bengaluru, Karnataka, India
Vehicle Detection with YOLOv2 (Python) (Keras, Tensorflow)
December 1, 2017 – Present
Investigating and understanding deep networks for object detection by using YOLOv2 model as an object detector. Also implemented NMS algorithm for removal of overlapping bounding boxes.
CNN’s for Image Recognition (Transfer Learning) (Python) (Keras, Tensorflow)
December 1, 2017 – Present
Hands on experimentation with multiple deep network architectures (VGG16, Inception V3, ResNet50) for image classification. The results were observed using multiple datasets.
Toxic Comment Classification (Python) (Keras, Tensorflow) (Lib -NLTK, Sklearn)
December 1, 2017 – Present
Evaluation of models (Bi-LSTM, CNN+GRU, NB-SVM) to detect different types of toxicity in the comments. Dataset used is comments from Wikipedia’s talk page edit.
Network Intrusion Detection (Python, Lib – Sklearn)
November 1, 2017 – Present
Classifiers - Logistic Regression, SVM, Decision Trees and Random Forests were used to detect if a network connection is good or malicious. A subset of KDD Cup ’99 was used as the dataset.
Image Recognition – Back propagation Network
November 1, 2016 – Present
Implemented fully connected back-propagation neural network using Theano to recognize the images in the CIFAR dataset. The report contained the information about the best hyper-parameters based on the Error loss graph and confusion matrix
Image classification using AlexNet
November 1, 2016 – Present
Implementation of AlexNet for Image classification using Keras and Tensorflow. The Classification results were observed using CIFAR – 30 dataset.
Neural Networks – Adaline Network and LMS
October 1, 2016 – Present
The program uses the Adaptive Linear Element to predict the price of a stock based on the historical price present in the data set. The learning algorithm adjusts weights based on those input and tries to Minimize the error rate using Widrow Hoff Least Mean Square algorithm.
Neural Network - Perceptron Learning Rule
September 1, 2016 – Present
The program models the perceptron Learning Rule that classifies linearly separable data by adjusting the weights based on the user Inputs.
Neural Networks – Pattern Recognition
September 1, 2016 – Present
The program learns the various patterns of Numerical digits using the variations of Hebbian Learning rule such as "Filtered Learning", "Delta Rule" and "Unsupervised Hebb" and recognizes the digits.
Cloud Computing
August 1, 2016 – Present
Configured EC2 as the webserver to host a python flask application with user authentication, which allows the user to upload, download, and delete images and files stored in S3.
DELTA (SIEBEL)
May 1, 2012 – July 1, 2015
DELL uses SIEBEL as a call center application for providing the services for the customers. DELL customer care has almost 30,000 users(agents) who use this application. This customer care application is part of the Delta application. The agent will create a Service Request (SR) under the customer service tag. Based on the SLA’s, damage severity and other parameters, agent will decide whether to create a replacement for the part or the software or other accessories. The data migration part in the project includes various functionalities like, loading delta agent information and responsibilities, service tag, customers, products related information into Delta database at regular intervals. Role & Responsibilities: Interacted with the Business Requirements and prepared Design documentation. Developed Complex database objects like Stored Procedures, Functions, Packages and Triggers using SQL and PL/SQL. Effectively made use of Table Functions, Indexes, Table Partitioning, Collections, Analytical functions, Materialized Views. Proactive database-level performance tuning, with a good working knowledge of Oracle internals. Extended the Siebel tables through Custom tables and columns. Implemented EIM best practices in mapping Siebel data with external data through EIM tables. Modified and designed windows batch jobs to load data in base tables. Involved in Data Mapping for loading the Legacy data and prepared complex SQL queries, configured and optimized configuration (.IFB) files to run EIM job for data load. Prepared the EIM jobs for bulk import of data.
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project history shows a strong focus on Machine Learning and Deep Learning, which aligns well with an ML Engineer role. The diversity of ML projects (object detection, image recognition, NLP, network intrusion detection) indicates a broad interest and adaptability within the ML domain. The prior experience as a Software Dev. Analyst at Dell, while not directly ML-focused, demonstrates enterprise experience and a structured approach to software development. The transition to ML/DL developer at Dell EMC further solidifies the career trajectory towards the target role. However, the lack of team-based project descriptions or collaborative work in the ML projects makes it difficult to fully assess cultural fit beyond technical alignment.
Soft Skills & Operational Fit
The candidate's previous role as a Software Dev. Analyst at Dell involved interacting with business requirements and performance tuning, suggesting an ability to understand operational needs and optimize solutions. The project descriptions are clear, indicating good communication skills in technical contexts. However, without psychometric test results, a comprehensive assessment of soft skills and operational fit is limited.