
Ketul Parikh
Project Trainee

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About me
DevOps Engineer | Backend Developer | 2 X AWS | Knowladge seeker | Autodidact | Inquisitive
Education

LDRP Institute of Technology & Research
2010 - 2014Bachelor of Technology (B.Tech.) Electronics and Communications EngineeringActivities and Societies: Coordinator at XENESIS - National Level Tech-festival. Volunteer at XENESIS - National Level Tech-festival.

Sheth C.M. High School and Higher Secondary School
2008 - 2010Intermediate/+2 (science) AGRICULTURE, AGRICULTURE OPERATIONS, AND RELATED SCIENCES
Mount Carmel High School, Gandhinagar ,Gujarat
1996 - 2008Schooling schoolingActivities and Societies: - Drawing competition - Dance participation -

Dhirubhai Ambani Institute of Information and Communication Technology
2014 - 2016Master of Technology (M.Tech.) Machine Intelligence
Experience

Embitech
Jul 2013 - Apr 2014Project Trainee-To provide best sort of vehicle to handy‐cap people. Apart from physical movement they can move wheel chair by speaking few words. Made the sample model,operated on 12 volts DC power supply- Analysed the integrated potential of ICs and sensors controlled by PIC16F877.- Developed a deep understanding of the Vocal command technology and its conversion to electrical signals.- Undertook the application of Easy Voice Recognize Module to convert human voice to electrical signals.- Familiarized with the ISIS Professional 7 Proteus tool which served the base of the entire project idea.- Successfully developed VOICE CONTROLLED WHEELCHAIR with two modes of the application.- Was awarded the highest ‘AA’ grade by Gujarat Technological University for the project. Show less

U
Aug 2014 - Jun 2016Title: Object recognition Using Deep Neural Network (DNN)Description: Given an image which has a single object, use Deep Neural Network approach to recognizing the object. Our goal is to learn the object model using the large dataset in an unsupervised way. We then obtain the features of labeled objects using this model. A supervised training approach is then used to solve the classification problem.To do so, I have done some prerequisites and introductory work:a) Supervised learning - Regression models and neural network on IRIS and WINE dataset.b) Unsupervised learning - Implemented Sparse Auto-encoders on MNIST and CIFAR dataset.c) Train an auto-encoder for estimating a Super-resolved image from the LR-HR dataset.d) Image Inpainting using Sparse Auto - encoder.e) For pre-training network implemented Restricted Boltzmann Machine (RBM).f) Implemented leNet-5 Convolutional Neural Network.g) Studied and implemented major UFLDL exercises. Show less
Researcher
May 2015 - Jun 2016Teaching Assistant
Aug 2014 - Jun 2016

PoshaQ
Jul 2016 - Jun 2021• Directed a team specializing in advanced attribute and garment size detection.• Implemented a dynamic model refinement and deployment pipeline, accommodating weekly data updates.• Crafted a customized pipeline to generate choice-based attributes based on detected poses, colors, and garments,in line with client requirements using Python and MongoDB.• Took the lead in developing internal payment management software, leveraging Node.js. • Developing CV-based attribute (Color) classification for further converting input images to a specific color.• Implemented CV-based attribute detection to eliminate shadows, adjust skin tones, and enhance background.• Collaborated with the QC team to create a correction dashboard for client attribute adjustments.• Successfully integrated with Google Cloud services for enhanced functionality and performance.
Product Lead
Aug 2018 - Jun 2021Data Scientist
Jul 2016 - Jun 2021
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Ventuno Technologies (P) Ltd
Dec 2020 - Jul 2021Back End DeveloperWorked as a backend developer.• Technology Used: Python, Django, Swagger, Postman• Developed REST API for OTT platform configuration• Used video encoding and decoding libraries in Python• Developed Unit test cases

Sentinel Insights
Aug 2021 - Nov 2023DevOps Tech Lead | Backend Developer- Developed Cloud Infrastructure on AWS- Built CI/CD using Jenkins and GitHub actions- Used the following AWS service i. Deployed services on AWS EKS ii. Used AWS S3, SQS, SES, and EFS for our ETL pipeline iii. Used AWS CloudWatch (X-ray, dashboards, metrics, alarms) for the observation and monitoring iv. Used IAM and CloudTrail for Security and Anomaly Detection- Built ETL pipeline using NodeJs- Worked on MongoDB and Snowflake for the data processing
Licenses & Certifications
- View certificate
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AWS Certified Developer – Associate
Amazon Web Services (AWS)Oct 2023 - View certificate

Machine Learning - A Case Study Approach
University of WashingtonJul 2016 - View certificate

R Programming
The Johns Hopkins UniversityJul 2016 - View certificate
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AWS Certified Cloud Practitioner
Amazon Web Services (AWS)Oct 2023 - View certificate

Programming for Everybody (Getting Started with Python)
University of MichiganJul 2016 - View certificate

Regression Model
The Johns Hopkins UniversityJun 2016 - View certificate

HashiCorp Certified: Terraform Associate (003)
HashiCorpDec 2023 - View certificate

Neural Networks and Deep Learning
DeepLearning.AIAug 2018 
German Language
DUALINGONov 2016- View certificate

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
DeepLearning.AINov 2019 - View certificate

Front-End Web UI Frameworks and Tools: Bootstrap 4
The Hong Kong University of Science and TechnologySept 2017
Languages
- enEnglish
- hiHindi
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