Jayakanth Gaddam

Key technologies used - (AWS - | EC2, ECS, Lambda, VPC, Codepipeline, ECR, MWAA, SQS, cloudformation, s3, cloudwatch |, Docker, Python, OOPS, Fast API, PostgreSQL, Opensearch, GitHub, Linux, AI Agents, Heroku )

• I have worked in a remote team environment with employees from various countries, in a product-based company, using the Agile method of working.
• I am deeply involved in understanding and migrating the company infrastructure from Heroku to AWS within the deadline.
• I worked on creating and automating the EC2 instance schedule according to the business use case, and saved resource usage and about 80 percent of the costs.
• Created a setup and used AWS MWAA (Airflow) as a cron job to trigger various scraper runs multiple times throughout the day. Used intrinsic functionalities within Airflow to limit the number of workers assigned for a DAG.
• Frequently used S3 to store and fetch data, relied upon CloudWatch to monitor resources and logs from code, and Systems Manager Resource to store parameters.
• I am responsible for managing IAM for all resources created and managed. Following the 'Principle of Least Privilege'. I also assisted my team members in creating roles and securing their IAM permissions.
• I have written Dockerfiles and used Docker to package the whole project as images to local and AWS (ECR). Worked extensively with variations in Docker setup (bash, shell), and have a firm understanding of Docker commands.
• I made use of VPC for the AWS resources I created by placing them in private and public subnets, according to the requirements. I created a VPN and Bastion host to make use of private subnet resources.
• Created VPC peering for resources placed in different VPCs in the same or different regions.
• Used and managed CodePipeline in AWS to automate Docker builds when changes were pushed to the selected GitHub branch in a project.
• Created AWS RDS in a private subnet, and maintained its state during my time in the company.
• Used CloudFormation scripts in YAML format to create and delete the resources in AWS according to the requirements.
• Worked extensively with Lambdas and also created a Python script to automatically update Lambda images for all.
• Created and managed Elastic Container Registry (ECR). Involved in creating various tasks with ECR using Fargate, suitable task definitions, and computing memory resources.
• I was responsible for managing costs and billing management for all the AWS resources, and was involved in cost-cutting by implementing suitable practices, making some resources redundant in the process.
• I extensively used the boto3 library (AWS SDK) to connect to AWS resources from Python, to interact programmatically and used this to greater scope.
• I used Python to connect to an RDS (Postgres) database in many cases and performed tasks such as inserting, updating, deleting, checksum, and others.
• I used Python to perform data engineering work as the data we collected needed adjustments, manipulation, checking, and appending, among various others.
• I expertly employed object-oriented programming concepts to make the code reusable and eliminate redundancy.
• I always maintained the code well and was involved in debugging others' code as part of my routine tasks.
• In our company, we used GitHub to streamline the code among team members. I have always followed the best practices that are put in place.

Key technologies used - ( Python, OOPS, PostgreSQL, GitHub, Linux, and Excel )

• My role included understanding concepts of cybersecurity, breaking them down into smaller sections, and creating questions accordingly for course students.
• Using various methods of the Python language to approach a framed question and explain briefly by adding comments.
• Developing test cases and writing the code efficiently to satisfy all the developed scenarios.
• Documenting the process and listing all the queries that need to be addressed for the client.

Key technologies used - ( Python, OOPS, Fast API, PostgreSQL, GitHub, AWS, Linux, and Excel )

• I was part of the data engineering team, which used Python to process the collected data from the pipeline into a specified format, and host on AWS in an Agile method of working.
• I used the rest framework (Fast API) to develop API endpoints, and efficiently route the queries to avoid bottlenecks and improve speed.
• This role helped me to have a strong foundation in data structures, error handling, dealing with edge cases, and employing testing as part of writing code in Python programming.
• In my work with the Rest framework, I have written CRUD operations, and created login and admin pages.
• I have worked with Postgres database and efficiently made use of the functionality of this relational database by writing well-structured queries, and by using Python code.
• I actively participated in debugging, code reviews, discussion of pull requests, and documenting the code.
• Involved in understanding and using AWS resources such as EC2, S3, CodePipeline, and VPC, among others, as part of the hosting process in the cloud.