Sep 2023 – May 2025 · 1 yr 8 mos · Hyderabad, India · On-site

Jul 2023 – Mar 2025 · 1 yr 8 mos

Jan 2023 – Jul 2023 · 6 mos

Feb 2022 – Sep 2023 · 1 yr 7 mos · Remote

Mar 2021 – Apr 2023 · 2 yrs 1 mo · Bhopal, Madhya Pradesh, India

Jul 2020 – Sep 2020 · 2 mos

• Mobile Application development both for android and ios using flutter by google...

Jun 2020 – Jul 2020 · 1 mo

• A. AWS (Public Cloud) :
• Setting up the system for AWS
• AWS Storage: The S3 Bucket
• The Root Module
• AWS Compute:
• AMI Data, Key Pair, and the File Function
• The EC2 Instance
• User Data and Template Files
• AWS Networking:
• VPC, IGW, and Route Tables
• Subnets, Security, and the Count Attribute
• AWS infrastructure : IAM policies, Auto-scaling,Lambda, Cloud Trail, Cloud Watch, RDS, Cloudfront
• AWS : HA Architecture
• Setup large-scale, high-traffic, redundant, cloud-based Iaas/PaaS/SaaS environment at a Public Cloud provider like AWS
• B. OPENSTACK (Private Cloud with Containers) :
• Openstack Overview
• o Nova architecture
• o VM provisioning walkthrough
• OpenStack Networking Overview
• o KVM networking with Linux bridges
• o Single‐host vs multi‐host networking
• o The role of Network Manager
• Floating IPs
• o Traffic Flow
• Openstack Storage Overview
• o The Ring, RingBuilder, partitioning
• o Account, container and object servers
• o Replication
• o Security/ACLs
• o Deployment and Operations
• Automated Installation of Openstack
• o Service Architecture
• o Openstack cookbooks – Management server, Keystone, Glance, Quantum (Neutron), Nova, Cinder, Horizon
• OpenStack Magnum
• o OpenStack's Container Infrastructure Management Service (Magnum)
• o Managing Clusters with Magnum Using the CLI and DashBoard
• C. Terraform (with AWS & OpenStack) :
• o Infrastructure As Code In AWS / OpenStack With Terraform
• o
•  Configuring and Creating AWS / OpenStack Resource with Terraform
• o Terraform Concepts
•  Terraform Concepts:
•  Resources ,Data Sources,Locals,Outputs,Templates And Files,Providers,Variables
• o Terraform Modules
•  Terraform Custom Modules and Registry Modules
• o Terraform State
•  Terraform State -
•  Managing Terraform Plans and Remote State
• o Multiple Environments & Resource Meta Parameters
•  Terraform Workspaces
•  Resource Meta Parameters
• o Terraform with OpenStack
•  Block Storage
•  Compute
•  Networking
•  Load Balancer
•  Firewall
•  Object Storage

Apr 2020 – Jun 2020 · 2 mos

• MACHINE LEARNING AND DEEP LEARNING:
• Worked in Redhat Linux [RHEL8 and RHEL7]
• Researched in various libraries of Python for Data Science and Machine Learning
• Learned TensorFlow code-basics working behind Keras
• Graph Visualization using various libraries seaborn, matplotlib….
• Linear Regression model with customization
• Logistic Regression model
• K Nearest Neighbor algorithm
• K-Means classifier algorithm
• Random Forest classifier algorithm for unsupervised learning
• Neural Networks (Deep Learning and Deep fake)
• Feature Extraction and Feature engineering
• Feed forward neural networks (FNN)
• Convolutional neural networks (CNN)
• Recurrent Neural networks (RNN)
• Generative Adversal Neural Networks (GAN)
• Restrict Boltzman Machine (RBM) for business model creation
• Long Short-Term Memory (LSTM)
• Collaborative Filtering with RBM
• Autoencoders & Applications in machine learning
• How to compose Models in Keras
• Saving and Loading a model with Keras
• Computer Vision using NN
• Text Data Processing
• Image / Video processing with various libraries
• Speech analytics - Speech to text / Voice tonality
• Transfer Learning
• Object Detection with SSD
• Neural Networks, Convolutional Neural Networks, R-CNNs , SSDs, YOLO & GANs
• Transfer Learning and using pre-trained models (VGG, MobileNet, InceptionV3, ResNet50) on ImageNet and re-create popular CNNs such as AlexNet, LeNet, VGG and U-Net
• Facial Recognition with VGGFace
• Executing, Annotate, Train and Deploy Custom Mask R-CNN models
• DEVOPS:
• DevOps CI/CD pipeline to build and deploy a project
• Git, Maven, Jenkins, Docker, Kubernetes
• Integration of CI/CD with ML/DL
• Deploy ML/DL model over kubernetes

Apr 2020 – Jun 2020 · 2 mos

• Understanding DevOps:
• Designing and implementation of Continuous Integration Continuous Delivery Release Management
• Architecture, Design, and Implementation of highly scalable and highly available solutions
• Monitor infrastructure, set up monitoring tools, review a regular basis and collect stats
• Monitoring, troubleshooting and diagnosing infrastructure systems.
• Infrastructure usage and monitor
• Organize log, event, and monitoring data/ stats and create reports for management
• Zero Downtime Deployment Light Dark Green Blue architecture
• Execute Unit Tests in CI/CD server Jenkins
• Incremental code testing and deployment
• Agile Project Tracking tools
• DevOps Tools in areas like CI/CD Environment Automation, Release Automation, virtualisation, infra as a code or metrics tracking.
• Docker & Kubernetes
• ELK Stack - (Elastic Search, Logstash, Kibana)
• Source Control Management (Git, GitHub & Gitlab)
• Jenkins: Multi-staged CI / CD with tools
• Introduction to Openshift (PAAS Cloud) with Docker/Podman and Jenkins pipeline
• Performance Monitoring Tools (Kibana, Grafana, Prometheus etc)
• C. Python Scripting Language
• Test execution and Reporting with Pytest framework
• Implement unit tests in python using the discipline of Test Driven Development(TDD)

Feb 2020 – Apr 2020 · 2 mos

• I got training in redhat linux 8 operating system from linux World CEO and founder.
• I can work efficiently on linux operating system with networking and pakage management.

Jul 2020 – Aug 2020 · 1 mo · Rajasthan, India

Aug 2019 – Apr 2023 · 3 yrs 8 mos · NIT, BHOPAL