• Designed the MlOps framework for a critical data platform in TD Bank to operationalize the machine learning and deep learning models safely, securely and swiftly.• Assess and design the subnet ranges for base infrastructure.• Designed and implemented complete Model life cycle flow in Azure suite, right from ideation of model to operationalization of model.• Using RBAC mechanism and Azure Active Directory, onboarded various teams such as Data Scientists, ML engineers, Model Validation and Model Risk Management onto cloud services.• Setup multiple environments required for the model life cycle.• Well versed with the Azure Machine Learning Studio architecture and Azure ML SDK.Worked with Lead and senior architects to design and vet the approvals from enterprise design council (ITS team) to onboard a renowned AI based application onto TD network. (Base tech stack: AKS, Postgres SQL, Azure Active Directory, Azure dynamic PVC, Blob storage, NGINX, ADLS gen2)• Comprehensive understanding of the model repository structure and the significance of each code file.• Integrated multiple azure components like ADB, ADF, ADLS and AZ ML through different networking patterns and procedures.

Designed and implemented CI/CD pipelines for one of the mighty platforms in TD Bank. The design has been vetted and approved by the enterprise design council (ITS team).• Proved my leadership skills by leading group engineers and deliver the team tasks within deadlines.• Deployed numerous infrastructure services which includes both IAAS and PAAS services using both Jenkins pipelines and GitHub Actions; Terraform acting as the backend of the deployments in both cases.• Acted as the deployment team lead for both Application core DevOps team and infrastructure build team.• Acted as the design specialist and designed various data flows for the platform, some such flows are On-prem Kafka to Azure Data Bricks through publisher-subscriber model; on-prem golden gate servers to the cloud VMS by installing golden gate software on Azure VMs using SALT formula; Connecting to Threat Metrix servers using SFTP process from Azure Data Factory and pull the data near-real time through micro batches.Ensured safe, secure and reliable code promotion with all proper checks and gates like App Sec scans (tools: SonarQube and Veracode) and different test environments (Func. Test/QA-SIT, NFT/Perf. Test-PAT).• Acted as the Data Engineer and developed ETL pipelines in ADF and developed Python notebooks in ADB for data analysis and transformations.• Acted as the Release manager where I used to plan all the releases without colliding with any of the Enterprise Freeze days or advisory days.• Evaluate, assess and submit all the network connections for the application build and submit any related tickets (Firewall, DNS and Load balancer) to the Infrastructure Network build team.• Monitor the traffic flow at the application network layer using Panorama (tool used to monitor all Palo-alto traffic).• Automated CI/CD workflows through Python and Bash scripts which cut down the deployment times by 40-50%.

• Deployed and managed Docker images sourced from GitLab, automating the build, test, and deployment processes to enhance efficiency and reliability.• Successfully maintained and optimized multiple on-premises Kubernetes clusters, ensuring high availability and security.• Integrated continuous integration and continuous deployment (CI/CD) pipelines using GitLab CI, enabling automated workflows to the Kubernetes environment.• Configured and managed Kubernetes networking components and ensured security best practices were followed, including the implementation of network policies and role-based access controls (RBAC).• Performed regular updates and maintenance on Kubernetes clusters, including upgrades to Kubernetes versions, applying security patches, and managing cluster components and certificate renewals• Hands-on experience with Splunk to monitor resource utilisation and application/server health.• Extensive knowledge and proven experience in creating the dashboards, monitors and alerts in Datadog to monitor application infrastructure.• Integrate Datadog and Splunk with ServiceNow to create Auto-Cut Incidents for P1/P2/P3 incidents Enabled different features of GIT like merge-checks, hard approval limits and proper branch protection.• Delivered Cloud architectural and deployment solutions for AWS Private and Public Cloud.• Managed several AWS accounts with multiple Virtual Private Cloud (VPC)s for production and non-production environments, with automation, build out, integration, and cost control as main goals.• Using S3 buckets and Glacier for storage and backup on AWS, created S3 buckets and managed rules for them.

• Responsible for application build and release, as well as writing automation scripts.• Hands-on experience with Linux/Windows server administration.• As the owner of Post Development Environments, collaborated with the architects on the SDLC Process.• Using the Continuous Integration tool Jenkins, completed Deployments and Builds on several environments.• MAVEN Scripts were written to automate the build process.• Used the forms.net programme to do client-side validations. • SHELL scripts are used to schedule, monitor, report on, and improve server performance.• Using Git, implemented and maintained branching and build/release strategies.• Assisted with the development and deployment of Java applications to various environments, including QA, UAT, and Production.