Jaswanth

Engineer with expertise in Agile, Waterfall, and DevOps methodologies, adept at optimizing SDLC through orchestrated end-to-end CI/CD pipelines and workflow automation. Demonstrated proficiency in configuration management, infrastructure as code, and container orchestration within cross-functional Agile teams. Specialized in implementing robust monitoring, alerting, and incident response strategies to ensure high availability and minimize downtime. Leveraged cloud-native services for seamless integration, enhancing overall system performance, and optimizing cost efficiency.

Carried out smooth migrations by completing in-depth assessments, creating customized migration plans, automating with AWS CloudFormation and Azure Resource Manager, working with cross-functional teams, and putting CI/CD pipelines in place for dependable and effective transitions.
Used Azure Kubernetes Service (AKS) for Microsoft Azure and Amazon EKS for AWS to manage the deployment of containerized apps for effective scaling and integration with cloud environments.
Contributed to the effectiveness and success of the project by using AWS Lambda and SageMaker to drive real-time statistical modelling.
Designed and implemented infrastructure as code (IaC) templates, leveraging tools like AWS CloudFormation or Terraform, to provision and manage the required AWS resources for SageMaker workflows.
Created cookbooks, registered nodes, and configured environments to implement Chef server for configuration management and configured instances in the AWS environment uniformly and in accordance with established policies by integrating AWS OpsWorks.
Combined the Azure SDK and Amazon SDK for Python to create scripts that automate and manage web servers and Load Balancers in AWS and Azure settings.
Facilitated data interchange within the Service Fabric application by establishing communication channels between Azure and AWS services and putting protocols and configurations in place.
Integrated Gitflow's master, develop, and feature branches in Bitbucket for efficient version control and collaborative development, and took use of AWS CodePipeline's trunk-based development for streamlined code management, testing, and releases.
Integrated SageMaker into the CI/CD pipeline, ensuring seamless integration of machine learning models into the development lifecycle.
Utilized choice or pass states in AWS Step Functions as activation points, setting up the trigger state and following stages for well-coordinated execution, starting workflows, and progressing to follow-up states conditionally or instantly.
Provided detailed description of the infrastructure components that must be maintained for the deployment of multi-cloud applications, such as EC2 instances, IAM roles, and Elastic Load Balancers.
Utilized the features of the S360 analytics tool to monitor trends in product usage, create reports on user behavior, analyze customer engagement data, and extract insightful data that will improve user experience and marketing tactics.
Installed, configured, and managed ELK (Elasticsearch, Logstash, Kibana) for robust log management within Azure and AWS environments.
Implemented security measures using Azure Network Security Groups, AWS Security Groups, and Azure Firewall for organizational security in both AWS and Azure public clouds.
Utilized Azure Monitor and AWS CloudWatch for comprehensive monitoring, ensuring optimal performance and operational insights.
Demonstrated mastery over a variety of AWS and Azure services, including AWS Lambda, AWS S3, AWS CloudWatch, Azure Log Analytics, AWS CloudFormation, and Azure Resource Manager.
Deployed IaC practices exclusively in AWS, utilizing Terraform to create version-controlled scripts for streamlined and consistent provisioning of cloud infrastructure, ensuring scalability and reproducibility.
Leveraged Docker Compose for local container orchestration, initiating Docker configurations to define multi-container environments, thereby simplifying the development, and testing of containerized applications through streamlined setup and orchestration of interconnected services.
Implemented Prometheus for metric collection and Grafana for visualization, configuring data sources to enable continuous monitoring with real-time visibility into system metrics and performance.
Leveraged AWS Lambda Layers to enhance serverless architecture, optimizing code reusability by organizing shared dependencies separately, and thereby significantly improving the efficiency of AWS Lambda functions.
Implemented real-time monitoring, executed routine API regression tests, and seamlessly integrated API testing into the CI/CD pipeline to assure ongoing reliability and early issue identification for existing functionality.
Implemented AWS Auto Scaling by configuring auto scaling groups and defining policies based on metrics such as CPU utilization or incoming traffic, enabling dynamic adjustment of computing capacity to match demand, ensuring cost-effectiveness, and optimizing resource utilization in response to changing workloads.
Enhanced software reliability and quality by employing Selenium for automated web application testing, simulating user interactions across browsers, and utilizing JUnit for Java application testing through systematic unit testing.
Developed incident response plans by leveraging AWS CloudTrail to capture, monitor, and analyze AWS environment activity, providing detailed insights into security-related events and enabling swift and effective responses to potential security incidents within the AWS infrastructure.

Implemented AWS solutions for improved interoperability and versatility in the cloud environment, I worked with development teams to seamlessly integrate Azure Service Fabric into the software development process, streamlining deployment and management within the Azure environment.
Continually optimized AWS and Azure services by applying best practices from both cloud platforms and staying up to current on upgrades from both Azure and AWS, this allowed software development processes to stay up to date with emerging technologies.
Adopted a hybrid strategy for data analytics, combining AWS services with Azure Data Lake Storage and Azure Databricks to conduct in-depth online data analysis.
Implemented smooth data integration, Azure Data Factory for ETL processes on AWS-equivalent services and Azure, and Power BI was utilized for perceptive data analysis and visualization across the AWS and Azure ecosystems.
Managed SageMaker environments by configuring and versioning machine learning models and associated artifacts.
Utilizing Python, Perl, and Bash scripting for automation activities in both environments, cloud services were implemented on both the Azure and AWS platforms.
Utilized AWS-equivalent technologies and the Azure VMware Solution to migrate Linux virtual machines for reliable hybrid cloud operations.
Used ETL/ELT procedures on Amazon and Azure to load, extract, and convert data, guaranteeing precise and fast business reporting in multi-cloud scenarios.
Using GitHub, Boto3, Bash, and Shell scripts for AWS and Azure CI/CD pipeline technologies, a cross-cloud approach was used to increase automation and efficiency.
Made use of Azure Container Instances on AWS and Azure comparable services for resource-efficient and scalable containerized application deployment, Helm charts were utilized for Kubernetes application deployment in both AKS and AWS.
Utilized Kubernetes to streamline the deployment process, orchestrating containerized applications across clusters, integrating it into CI/CD pipelines for automated build, test, and deployment stages, while also implementing auto-scaling and load balancing for optimized resource utilization and high availability during peak traffic periods.
Automated deployments to Azure Kubernetes Service clusters and their AWS counterparts were made possible by the integration of Argo CD with the CI/CD pipeline on Azure and AWS.
Used Bitbucket (previously Azure DevOps) for code management and version control, connecting with AWS and Azure CI/CD pipelines to facilitate effective cross-cloud cooperation.
To provide early problem detection in apps launched on Azure, SonarQube was integrated into the Azure CI/CD pipeline for code quality analysis, this technique was then expanded to AWS environments to provide thorough code quality assurance.
Used Git to manage AEM template changes in Azure DevOps Services, guaranteeing version control for consistent template management across AWS and Azure systems.
Set up and maintained a range of Azure services, such as Blob Storage, CDN, Load Balancer, Service Bus, DNS, SQL Database, Azure Virtual Machines, and Virtual Networks, concurrently managed AWS services for similar capabilities, guaranteeing consistency in deployment.
Used AWS services to improve coordination in multi-cloud DevOps workflows and Azure Queue Storage to facilitate dependable communication across remote systems on Azure.
Developed and implemented extensive API testing scenarios in conjunction with development teams, encompassing both positive and negative behaviors, to provide full test coverage for a range of use cases.
Implemented comparable AWS security mechanisms for protecting sensitive data across both cloud platforms and enforced strict security and compliance standards utilizing Azure Web Application Firewall and Azure Security Centre on Azure.
Implemented monitoring solutions for SageMaker endpoints to track model performance, resource utilization, and overall system health.
Collaborated with data scientists to understand their requirements, provide guidance on deploying models in SageMaker, and assist in troubleshooting deployment issues.
Employed Azure Notification Hubs to facilitate easy communication between dispersed systems on Azure, expanded this approach to AWS to ensure reliable and prompt notifications in multi-cloud settings.

Utilized Google Cloud Platform (GCP) for backup and restore services, network design and configuration, subnets, DNS setups, security rules, and routing, used AWS services for supplementary solutions, like Amazon S3.
Utilized AWS equivalents in conjunction with GCP capabilities, such as Cloud Storage, Cloud Identity, Google Pub/Sub, and Cosmos DB.
Exhibited expertise in Google Cloud Load Balancing, VM instance creation in managed instance groups, GCP scalability and availability, and integrating AWS services for complete infrastructure resilience.
Applied AWS solutions in addition to Google Cloud Backup, Google Cloud Site Recovery, and Google Cloud Site Reliability Engineering (SRE) concepts for virtual machine backup and restoration.
Used Python in GCP's Cloud Build to automate tasks, improve workflows, and guarantee a smooth integration and added Amazon Lambda for more automation power.
Integrated Git with AWS Cloud Source Repositories and Google Cloud Source Repositories were used for effective version control procedures.
Used Terraform and Deployment Manager templates, infrastructure was built, deployed, and transferred to GCP while integrating AWS CloudFormation for cross-cloud infrastructure management.
Made use of S360's real-time measurements for resource optimization, integrating AWS CloudWatch for seamless, all-encompassing monitoring.
Incorporated Deployment Manager into GCP practices for infrastructure-as-code implementation, AWS CloudFormation has been added to provide standardized multi-cloud CI/CD pipelines.
Used AWS ECS for container orchestration and Google Kubernetes Engine for containerized application deployment, doing away with infrastructure management.
Used Cucumber, SonarQube, and JIRA, integrated REST API testing into the CI/CD pipeline to guarantee thorough testing of RESTful services with AWS counterparts.
Used Python scripts and Ansible, automated monitoring was implemented for alarms and notifications using AWS CloudWatch and Google Cloud Operations Suite.
Used GitLab in GCP DevOps projects, utilizing its collaborative code management and version control features in conjunction with integrated CI/CD pipelines and AWS CodePipeline.
Used Python and Jenkins to create microservice onboarding tools that made it simple to create and manage build tasks, Kubernetes deployment, and services in GCP, AWS Lambda was added for improved serverless capabilities.
Utilized Splunk for log analysis and enhancing server performance; set up Splunk to integrate with GCP services and Appian applications for alerts and reporting, expanded to utilize AWS services for thorough log analysis.

Improved features in the agent compensation/client information system in a mainframe setting, with an emphasis on producing commission statement reports and determining agent and management remuneration.
Carried out thorough validation, which included system, integration, and unit testing, found, and verified problems; and produced and examined artefacts, such as test script results, for quality control.
Created a C and C++ software repository, using package dependency graphs to store and retrieve packages efficiently, and handling XML meta-data.
Utilized Win Forms/WPF to develop the client interface, Sockets/WCF to integrate communication services, and JavaScript client-side scripts with CSS for browser formatting were incorporated.
Created a basic SSL VPN in C utilizing Ubuntu's TUN/TAP virtual interfaces and OpenSSL Library functions, integrating contemporary features like public key infrastructure (PKI), key management, encryption, authentication, and integrity.
Controlled networking setups, guaranteeing smooth communication and information sharing inside the mainframe system.
Implemented repository functions, allowing users to check in and check out packages and display the contents of the repository under version control.
Employed scripting languages to automate jobs, boosting overall efficiency in the Mainframe environment and streamlining system operations.
Designed and implemented a modified bandwidth estimation algorithm to monitor agents, effectively detecting new forms of Denial-of-Service attacks in under-provisioned Cloud Data Center environments.
Performed maintenance, tracking, and troubleshooting as part of system administration responsibilities to guarantee the Mainframe system's optimum performance and dependability.