Prosenjit Das

• Modernized and operated AWS infrastructure for the Airfleet WordPress software platform, migrating from traditional EC2-based deployments to Amazon ECS and Fargate. Reviewed and improved the existing architecture for reliability, scalability, security, networking, autoscaling, and production readiness.
• Configured and supported ECS clusters, services, task definitions, Fargate workloads, IAM roles, security groups, Application Load Balancers, health checks, deployments, and autoscaling. Configured AWS CloudWatch for centralized metrics, logs, alarms, dashboards, and production incident investigation.
• Integrated CloudWatch with Grafana and worked with AWS Managed Grafana to modernize operational dashboards and alerts. Upgraded Grafana through multiple major versions and transformed monitoring from legacy EC2-centric views to ECS and Fargate service, task, container, application, and infrastructure-level observability.
• Performed rapid troubleshooting and root-cause analysis for ECS task failures, container restarts, unhealthy targets, deployment issues, resource exhaustion, networking problems, IAM permission errors, and application failures.

CONTRIBUTIONS
• Collaborated with QA teams to enforce best practices across PR gates, branching strategies, API design, and database optimization, ensuring high code quality and strong testability. Guided QA set up to avoid rework and enable efficient validation across environments.
• Mentored development teams (Flutter, PHP/Laravel, and AWS) in adopting scalable architecture, secure coding standards, and DevOps practices, reducing technical debt before launch.
• Advised on scalability and security readiness, including IAM roles, secrets management, API performance tuning, and monitoring/alerting implementation.
• Defined CI/CD pipelines (GitHub, Bitrise, and QA/Prod) with PR checks such as linting, automated tests, and coverage enforcement to ensure reliable releases.
• Implemented enterprise workflow automation using n8n for DevOps orchestration, QA triggers, incident response, and cross-system integrations, improving operational efficiency.
• Created lightweight architecture documentation for investors and future hires, clearly outlining system design, infrastructure, and scalability strategy.

CONTRIBUTIONS
• Built HIPAA-compliant AWS infrastructure to support a private medical clinic’s patient database and middleware platform.
• Designed and managed a dedicated AWS account under a new LLC, ensuring account segregation, governance, and billing alignment.
• Implemented a secure VPC with RDS for storing patient PII, integrating AWS Secrets Manager for credential management.
• Developed and maintained a Python-based pipeline to sync patient identifiers into GoHighLevel (GHL).
• Oversaw resource migration and re-establishment under proper entity ownership to maintain compliance and operational consistency.
• Drove compliance and security initiatives, including HIPAA alignment, Business Associate Agreement (BAA) coverage, and comprehensive documentation.
• Configured AWS Artifact and related compliance tools to streamline regulatory reporting and support audits.
• Established root account access controls and enforced audit-ready security practices.
• Provided strategic guidance on early-stage infrastructure to ensure scalability, security, and compliance.
• Led two critical initiatives:
Phase 1: Build a secure, modular middleware pipeline to GHL.
Phase 2: Audit and harden the patient database infrastructure.

I designed a cost-optimized live streaming service for video-on-demand (VOD) and concurrent streaming.

The client, a growing media platform, sought to enhance its cloud architecture to support increased user demand and efficient data management. With an existing VOD application for uploading, streaming, and ad revenue generation, this project focuses on architecting and implementing a cost-effective live streaming service as part of their minimum viable product (MVP).

KEY RESPONSIBILITIES
• Assessed the existing AWS architecture, including high-level architecture (HLA) originally designed with AWS pro-services, and identified optimization and cost reduction opportunities.
• Architected and implemented a lean, scalable live streaming service using AWS IVS and Media Convert, ensuring it integrates seamlessly with the current VOD platform.
• Developed Serverless API using AWS Lambda and Node.js to process back-end jobs while handling media streaming and data conversion.
• Built an AWS Lambda function to process IVS metrics, calculate costs, and push data to Airtable using its API.
• Automated the financial workflow using XERO API and MAKE API.
• Developed Terraform, AWS CloudFormation, or GitHub Actions.

WORK DONE
• Developed a digital wallet-based payment system for membership clubs, integrating Google Wallet and Apple Wallet APIs to issue and manage member passes, enabling secure closed-loop transactions through mobile devices.
• Engineered scalable back-end functionality using Node.js, collaborating closely with internal engineering teams to align system architecture with business and user needs.
• Supported the client’s long-term vision to build a competitive alternative to mainstream payment platforms by ensuring modular design, performance, and integration flexibility.
• Designed and implemented intent-driven retrieval-augmented generation (RAG) assistants leveraging Azure OpenAI, Cognitive Search, and CosmosDB to deliver citation-backed, structured responses aligned with compliance standards for public sector clients.
• Built and maintained data pipelines for ingesting and transforming government datasets and public APIs, with a focus on transparency and regulatory compliance.
• Created and managed prompt engineering workflows, ensuring consistent, compliant output generation in high-stakes environments.
• Collaborated with cross-functional teams—product, engineering, and client stakeholders.

The Haydenfilms Institute is a growth-oriented 501(c)(3) nonprofit organization, where I developed, deployed, and maintained cloud-native applications on AWS, worked with AWS services Lambda, EC2, Docker, S3, RDS, DynamoDB, API Gateway, and CloudFormation, and developed and managed containerized applications using Docker and Kubernetes (ECS/EKS).

The organization's mission is to empower, educate, and fund independent and student filmmakers worldwide. We execute this mission through Hayden film festivals, screenwriting competitions, and other impactful programs.

Worked as an AWS DevOps and app developer to perform the following tasks:
• Developed Infrastructure-as-Code (IaC) using Terraform, AWS CloudFormation, or GitHub Actions.
• Implemented CI/CD pipelines using AWS CodePipeline, Jenkins, and GitHub Actions.
• Built and optimized web applications using React.
• Worked with real-time databases such as DynamoDB and Kinesis data streaming technologies.
• Optimized the application performance, security, and cost efficiency.
• Collaborated with cross-functional teams to integrate AWS solutions.

A digital payment wallet app for Android that allows users to make payments, buy products online, pay bills, and so on. Besides liaising with the front-end and back-end developers to design and develop the entire application and deliver a cloud-ready solution, I was the architect for this app's design, performing the following activities:

MY RESPONSIBILITIES
• Proposed a new digital wallet architecture that is extensible across end users, vendors, and bank applications.
• Designed a solution for payments to and from bank accounts and wallet-to-wallet or wallet-to-bank fund payments.
• Built a solution for merchant payments and bill payments.
• Created a cloud-ready solution with a DevOps and security pipeline to deploy any part of the service in a single run.

This project focuses on building robust integrations with Workday APIs, explicitly targeting the Applicant Tracking System (ATS) and recruiting functionalities. The goal is to streamline recruitment processes and ensure seamless data flow between systems. Key aspects include:

WORKDAY API DATA MAPPING
• Guiding developers to identify, access, and utilize relevant data endpoints within Workday APIs.

CONCEPTUAL FRAMEWORK OF WORKDAY OPERATIONS
• Providing a comprehensive understanding of Workday's operational architecture and interdependencies to enable efficient integration.

RECRUITING FUNCTIONALITY IMPLEMENTATION
• Assisting in implementing Workday's diverse recruiting features, including candidate tracking, job requisitions, and talent acquisition workflows.

BUSINESS PROCESSES AND WORKFLOW OPTIMIZATION
• Delivering actionable insights on configuring and leveraging WoWorkday'susiness processes and steps for enhanced operational efficiency.

CANDIDATE STAGE MANAGEMENT
• Strategizing and executing effective movement of candidates through various recruitment stages, ensuring compliance with organizational policies and goals.

An Azure cloud integration solution to move the client's on-premise retail applications, middleware solutions, and 1,000+ integrations on Azure cloud. As a cloud integration architect, I built cloud strategy and modernization across distributed clouds, including public, private, and hybrid clouds.

Key responsibilities:
• Assessed, mobilized, migrated, and modernized to help customers achieve migration goals.
• Designed an event-driven platform for a client business unit that lets loosely coupled applications and microservices produce and consume events using the Solace event portal and Azure Event Grid.
• Led the client's technical services team to design new architectures in the Azure platform to support the EOL of legacy applications.
• Designed and executed data migration strategies using Databricks
• Designed enterprise Integration solution using Azure PaaS services such as Azure Logic Apps, Data Factory, Synapse, Service Bus (claim-check pattern), APIM, and Azure Functions to decommission BizTalk from an on-prem data center.
• Developed data mapping using C#.
• Reduced application load time by 40% by implementing caching and optimizing database queries.
• Built and maintained a C# application used by over 10,000 users daily.

A serverless Azure IPaaS application for a retail company to create easy checkout across their stores. As an Azure architect, I designed and developed the entire workflow, which included the following activities:

• Configuring webhooks to trigger Azure Logic Apps for custom applications and services, such as processing order data from D365 API, using a Liquid template and XSLT mapping for transformation, and integrating Slack notifications for production monitoring alerts.
• Developing Azure-specific SOAR like Azure Sentinel for threat detection and automated responses within the Azure environment.
• Utilizing Azure Logic Apps to orchestrate automated incident management and reporting workflows.
• Restricted app execution on a compromised machine using Azure Sentinel. This involves orchestrating a response workflow that detects malicious activity.
• Automating the deployment of Logic Apps using Azure Resource Manager (ARM) templates and Bicep files.
• Developed data mapping using C# in function app with .NET 6 framework.
• Used OpenAI Studio for doc search and report creation.
.Designed and executed data migration strategies using Databricks with the help of Azure Data Factory and Azure Synapse.

An IoT and streaming real-time analytics-based project to allow cycling fans to track the race in real time, alongside viewing performance metrics of their favorite teams, through an application function.

ROLE AND RESPONSIBILITIES
Acted as a cloud architect in performing the following activities:
• Developed a responsive platform that could capture and persist the network packet and IoT data packets in real time using Azure IoT Hub, Event Grid, Logic Apps, SQL storage, and Power BI.
• Designed a real-time data processing platform.
• Designed real-time visualization that brings on-bike telemetry data to cycle fans globally in an engaging manner during races.
• Broadcast telemetry packets and insights to the fans and TV broadcasters on a real-time basis with high scalability.

This project focused on migrating monolithic .NET Framework functionality to AWS cloud platforms to enhance customer order processing with improved availability and reduced infrastructure costs. I collaborated with platform teams to evaluate swap vs. native memory usage in Kubernetes, enabled and configured swap memory on Kubernetes worker nodes to support RAM-intensive workloads, and implemented swap-aware configurations to ensure workload stability and provided rollback documentation.

ROLE AND RESPONSIBILITIES
• Conducted migration readiness assessments using the AWS Cloud Adoption Framework to identify platform gaps.
• Planned and executed migration using AWS Migration Hub for discovery, assessment, and tracking.
• Designed end-to-end microservices architecture leveraging Spring Boot and AWS Cloud services.
• Developed CI/CD pipelines for automated deployment using modern DevOps practices.
• Led user acceptance testing (UAT), production deployment, and warranty support.
• Migrated on-prem databases to AWS RDS (SQL), eliminating operational overhead.

This highly scalable rights management system enabled the client to manage their media channel rights and portfolio of linear and nonlinear rights in real time. It is a cloud-based one-stop shop for global distributors to buy and sell external channel rights.

RESPONSIBILITIES
• Designed E2E system architecture using microservices architecture on AWS.
• Developed microservices using Spring Boot, external interface through AWS API Gateway, and CDN.
• Set up an E2E DevOps CI/CD pipeline with Jenkins, Amazon ECS, and ECR with Ansible.
• Automated infrastructure setup using Terraform for Amazon ECS, ECR, EC2, and VPC configuration.
• Automated testing, conducted performance testing, deployed UAT, and executed data migration tasks.
• Designed high-availability applications on AWS across availability zones and availability regions.
• Integrated a NoSQL Graph DB (Neo4j) with Spring Data for rights management.
• Created the infrastructure and environmental management plans.
• Created the rollout plan for the software products and services releases.
• Designed scripts for automating build and deployment processes.
• Automated policy, configuration, and cloud formation templates using a Python SDK and AWS DevOps with Terraform scripts.

• Worked on data migration strategies using Databricks in an Azure environment.
• Developed and maintained efficient data pipelines using PySpark and Spark SQL for extracting, transforming, and loading (ETL) data from Onprem SQL and external APIs.
• Collaborated with data scientists and analysts to build interactive dashboards and visualizations on Databricks for data exploration and analysis.
• Worked on troubleshooting performance issues and ensuring security compliance during migration.

ACTIVITIES PERFORMED
1. Azure: migrating on-premise data to the cloud.
2. Transforming data using Azure Databricks.
3. Loading data into Azure Synapse: building a modern data warehouse.
4. Building dashboards: visualizing data with Power BI and Azure Synapse.

This project focused on the seamless migration of an enterprise-grade, on-premises Kubernetes cluster to Amazon EKS on AWS. The source cluster supported over 1,000 nodes hosting microservices across multiple domains, serving mission-critical applications with high availability and performance requirements.

KEY OBJECTIVES:
• Migrate workloads with zero downtime.
• Achieve improved scalability, manageability, and cost optimization.
• Enhance security, observability, and DevOps automation capabilities in the cloud.

SCOPE OF WORK:
• Assessment and planning: Performed workload inventory, cluster sizing, and dependency mapping. Developed migration strategy including node group planning, VPC design, and IAM policies.
• EKS infrastructure setup: Provisioned scalable EKS clusters with managed and self-managed node groups across multiple AZs for fault tolerance and auto-scaling.
• Networking and security: Implemented VPC CNI plugin, Calico network policies, and fine-grained IAM roles for service accounts (IRSA). Set up private subnets and NAT gateways to support hybrid network traffic.
• CI/CD integration: Refactored existing pipelines (Jenkins, GitLab CI/CD) for GitOps-based deployment using ArgoCD and Helm charts.

As an architect, I helped a significant manufacturing company aggregate vast amounts of data from global ERP systems and 3rd-party APIs like Twitter and LinkedIn, expecting future growth to exceed petabytes. I designed a data pipeline with the following:

• Ingestion: Event-driven architecture using AWS API Gateway and Lambda for real-time processing.
• Processing: A serverless ETL pipeline with AWS Glue, orchestrated dynamically via AWS Step Functions.
• Storage: Amazon S3 as a central data lake with data loaded into Snowflake for analytics and Amazon Redshift for BI queries.
• Analysis and ML: Amazon SageMaker enabled predictive modeling, such as customer apparel size recommendations with real-time inference.
• Consumption: Predictions cached in Amazon ElastiCache (Redis) and exposed via microservices APIs on a scalable AWS infrastructure.

This solution enhanced efficiency, scalability, and real-time decision-making across the enterprise.

Acted as a cloud-native architect and consultant for one of the largest British public service television and broadcast networks to design enterprise-level solutions. I migrated a legacy application built using Jakarta EE, EJB3, and WebLogic stack to digital services (refactoring and rearchitecting) on AWS cloud following SOA and microservice best practices and building DevOps practices inside the project.

Responsibilities:
• Worked in container-based technologies like Docker and Kubernetes to design the solution.
• Implemented business use cases in Jakarta EE and the latest Spring Framework.
• Integrated Docker container orchestration framework support using Kubernetes by creating pods, ConfigMaps, and deployments.
• Helped the client migrate from a legacy application to a cloud-ready solution.
• Contributed to application design and implemented following best practices.
• Migrated on-premise WebLogic Server to Red Hat JBoss container on AWS cloud.
• Designed and developed the entire cloud infrastructure automation stack using Terraform.

U.S. Bank is one of the five largest banks based in the USA, operating all over the USA. It is a full-service bank with offerings for retail, corporate, investment, and wealth customers.

U.S. Bank engaged IBM to migrate their current banking solution built upon Connect Enterprise to AWS cloud to offer scalability and flexibility and provide core digital services that are more user-friendly and as per customers' needs. It involved the migration of 8,500 partners and their internal applications. I re-architected the full solution on the cloud based on the microservice-based architecture and API-first approach. Designed highly scalable digital solutions that cater to high traffic without any issues. Improved the user experience with a full solution of mobile banking.

RESPONSIBILITIES
• Involved in discussions with the bank's team on corporate, investment, and wealth management in terms of their partner onboarding process.
• Designed and implemented migration workflow and reporting framework using IBM Cloud and Cloudant.
• Involved with the IBM SWG team in the course of Sterling MFT framework design.
• Designed and implemented custom integration between the bank's internal application and Cloudant (DBaaS).

I collaborated with IAG (British Airways) to design a scalable solution for improving check-in operations during peak volumes, where long queues affected flight processing and demanded more trained staff, where we modernized the platform into microservices deployed on AWS EKS, ensuring elasticity and resilience at both infrastructure and application layers, I enabled and tuned Kubernetes swap memory on worker nodes for memory-constrained environments, and implemented swap-aware configurations and validated impact on pod eviction, ensuring workload stability under memory pressure.

Technologies used included AWS Redis, Amazon API Gateway, Keycloak IAM, Eureka Discovery, Zuul, Amazon SQS, Java, Spring Boot, Node.js, Angular, and AWS CloudWatch. This transformation allowed IAG to rapidly update services and integrate seamlessly with Amadeus, the back-end industry booking system.

KEY CONTRIBUTIONS
• Delivered DevOps solutions within Agile practices.
• Developed Java/Jakarta EE use cases using the latest Spring Framework.
• Designed microservices architecture with Spring Boot and Spring Cloud.
• Led capacity planning, cost, and effort estimations.
• Modernized a legacy system by collaborating with domain architects.

A French pharmaceutical company is conducting clinical trials for a new drug. The goal is to predict patient response based on genetic markers, demographic data, pre-existing conditions, and lab results. This enables the identification of likely responders and supports more efficient, cost-effective treatment planning.

I was responsible for setting up the end-to-end DevOps pipeline to support this initiative, covering the following key areas:

• Data engineering (CI pipeline)
• Model training and evaluation (CD pipeline)
• Model deployment

TECHNOLOGIES AND COMPONENTS USED
• Azure DevOps Repos: Source control for data preparation, model training, and deployment scripts.
• Azure Pipelines: Automate training, evaluation, and deployment of the machine learning (ML) model.
• Azure ML: Manage experiments, track models, and handle model registry and deployment.
• Azure Key Vault: Secure storage of encryption keys for patient data.
• Azure Blob Storage: Storage for encrypted datasets.
• Azure Kubernetes Service (AKS)/ACI: Host and serve the ML model for real-time inference.

Worked with Teva on a patient platform to store, retrieve, and analyze data from patient-facing apps, medical devices, and other digital interfaces. This initiative registers patients and their smart medical devices to track the usage of medication and capture feedback on a real-time basis. The proposed solution uses data captured from the devices to provide patients with the necessary support to prevent and minimize the occurrences of exacerbations.

RESPONSIBILITIES
• Worked on iOS mobile app integration with the back-end IBM Watson Health platform.
• Worked on API design and data transformation mapping using IBM Integration Bus 10.0.
• Worked on IBM Watson analytics to capture and process a large amount of patient data from medical devices and built reports based on it
• Helped the team with the technical troubleshooting process.

Celgene Corporation is an American biotechnology company that discovers, develops, and commercializes medicines for cancer and inflammatory disorders. It is incorporated in Delaware and headquartered in Summit, New Jersey. Celgene is the world's tenth-largest independent biotech company by market cap.

The vision of the Watson Patient Safety (WPS) system was to transform pharmacovigilance to drive the new era of patient safety by introducing innovative approaches that exploit cognitive computing and machine learning to increase operational efficiency, consistency, and quality of data collection and signal detection while enabling Celgene to handle increasing case volumes, access new data sources, and predict potential safety signals.

Watson Health Cloud (WHC), built on the IBM Watson platform, was the core framework leveraged in this solution. Both cognitive from WHC and non-cognitive, custom-built services are part of this solution. This engagement was executed in four phases.

RESPONSIBILITIES
• Making complex technical decisions.
• Managing offshore delivery.
• Oversaw technical delivery on Java, including IIB, business process management, Java microservices, and testing.

The project is an outsourcing model. We need to develop and test EDI map specifications for their various clients worldwide. The scope of work includes understanding the purchaser’s EDI guidelines, finding the GAP between the application layout and the purchaser’s requirement, preparing a test plan, and testing for successful staging and production.

ROLE AND RESPONSIBILITIES
• Developed, debugged, and deployed EDI maps.
• Ensured that – as per the requirements, maps were developed and delivered, meeting the deadlines.
• Clarified and finalized the field mapping details (FMD) by communicating with concerned people at Axway.
• Solved all kinds of technical problems.
• Developed advanced coding concepts for future implementation.

Development and maintenance of maps related to all kinds of messages, such as 850, 856, 810, and 855, etc., in X12 standards and DESADV, ORDERS, ORDERS, and ORDCHG in EDIFACT standards. Creating TP in a test environment and moving the map in a test environment and, after successful testing, moving the map to the production environment. Also involved in providing support to clients.

ROLE AND RESPONSIBILITIES
• Developed, debugged, and deployed EDI maps of complex messages like ASN and Remittance Advice for both X12 and EDIFACT standards (inbound and outbound).
• Performed technical analysis and troubleshooting on complex problems.
• Understood business rules and needs.
• Identified root causes for technical problems and provided timely resolutions.
• Reported status updates to the client as well as to the delivery manager.
• Solved problems through strong analysis and troubleshooting.
• Created and executed test data and test plans.
• Conducted unit testing using various test case scenarios.
• Interacted with clients on a daily basis.

Nestlé GLOBE is probably the largest SAP project ever attempted globally. This project aims to unlock Nestlé’s business potential by creating and adapting global common business processes. It is one of the biggest SAP offshore projects in IBM Global Delivery but contains many different technology platforms apart from SAP. It integrates all operations of Nestlé across the globe. The implementation would be in over 80 countries covering all SAP modules, including CRM, BW, APO, etc.

A wide-ranging EAI/EDI solution for interfacing with a mammoth number of legacy applications and trading partners is also in scope, which involves designing a complete Middleware Integration Solution. IBM MQ Series 5.2, Mercator, and WDI are used as the EAI/EDI tools at the core. WDI is used as an EDI Translator; Cyclone Interchange is used as a communication tool for VAN and EDIINT connectivity (AS1, AS2) with trading partners. As a part of the migration of the middleware to a new solution, SAP’s eXchange Infrastructure (XI) has been chosen as the next-generation solution.

Mead Johnson Nutritional’s is a world leader in providing innovative, science-based nutritional products for infants and children. Notably, the company has pioneered products that help those with inborn metabolic disorders grow and develop to their full potential.
Throughout 2006, Mead Johnson's ENFAMIL infant formulas, toddler formulas, and children's nutritional products continued to earn the trust of parents and healthcare professionals worldwide. The company's intercontinental business was distinguished by strong sales and growth in all regions, most notably in Asia and Latin America.
Mead Johnson has chosen IBM as their partner to:
• Implement a robust but less complex SAP system to support Mead Johnson’s business and expected growth.
• Migrate, rationalize, and integrate key legacy EDI applications.
• Design, implement, and operate efficient, globally delivered F&A business processes that are scalable to support growth.

ROLES AND RESPONSIBILITIES
• Project scope identification and process set up.
• Team building with WTX, WMB, and WPG skills.
• Delivery of functional/technical specifications for EDI and non-EDI maps.
• Integration architecture landscape design and implementation from offshore.

The BMW Group, a leader in premium automobile and motorcycle manufacturing, implemented the Red Hat OpenShift Container Platform to streamline business applications and services delivery.

The solution involved containerizing existing monolithic applications using OpenShift-supported templates for messaging and logging infrastructures. A key feature was the API Gateway implementation, which facilitates user interactions such as registration, login, service requests, and order tracking through RESTful API calls using Apache’s HttpClient.

ROLES AND RESPONSIBILITIES
• E2E system architecture: Designed and implemented a microservices-based architecture.
• Microservices development: Built scalable, reliable microservices using Spring Boot.
• Agile planning: Participated in epic, feature, and user story creation for iterative UI and application development.
• CI/CD pipelines: Established an end-to-end DevOps pipeline for continuous integration and cloud delivery.

The client aimed to modernize their eCommerce operations by migrating from a legacy Magento 1.9 store to BigCommerce, with a focus on improved performance, mobile responsiveness, better integrations with ERP/CRM, and an enhanced customer experience.

• Designing and developing custom BigCommerce Stencil themes.

• Migrating legacy catalog data (products, categories, customers) to BigCommerce.

• Building custom APIs and middleware for ERP and CRM integrations.

• Setting up BigCommerce headless architecture for enhanced performance (optional front end).

• Developing custom checkout enhancements using BigCommerce Checkout SDK.

• Implementing third-party apps (loyalty programs, reviews, live chat) and custom scripts.

• Optimizing store performance (lazy loading images, minimizing third-party scripts).

• Collaborating with marketing and UX teams to deliver a pixel-perfect storefront.

A mid-sized finance team receives supplier invoices via email. These invoices need to be:

• Stored securely in Google Drive.
• Logged into a central spreadsheet (Google Sheets).
• Approved via Slack by the finance manager.
• Automatically updated once approved or rejected.
• Tracked in real time for audit purposes.

Using n8n, we automated this entire approval flow without writing custom back-end code or deploying infrastructure.

Designed and implemented a cloud-native, event-driven platform for ingesting, processing, and analyzing high-volume IoT telemetry from connected cycling devices. The solution leveraged Azure Integration Services, Apache Cassandra, Kubernetes, and microservices to deliver near real-time race analytics, live dashboards, secure APIs, and scalable data processing for global sports events.

As a GCP DevOps and cloud platform engineer, I supported enterprise cloud migration and modernization initiatives. I designed and operated production-grade Google Cloud environments with a strong focus on Google Kubernetes Engine (GKE), Kubernetes, networking, IAM, Infrastructure as Code, CI/CD, observability, and production operations.

Served as the senior software architect and HIPAA security advisor for a healthcare-focused AWS platform, leading cloud architecture, security assessments, and compliance advisory. Conducted AWS well-architected framework (security pillar) and HIPAA readiness assessments across 60+ security controls, providing actionable recommendations to strengthen governance, encryption, identity management, monitoring, and incident response. Designed a scalable, HIPAA-ready cloud-native architecture supporting secure video messaging, social networking, and real-time communication, while guiding infrastructure teams on security-by-design principles, AWS best practices, and long-term scalability. Collaborated closely with client stakeholders and engineering teams to establish a secure, resilient, and compliant AWS environment capable of supporting significant future growth.

Designed and developed an AI-powered Security Investigation Agent leveraging the Claude API to automate the triage and investigation of AWS security incidents. Built an event-driven solution that ingested high-severity findings from AWS GuardDuty, Security Hub, and CloudWatch Alarms, correlated related events across AWS services, and generated comprehensive incident timelines with impact assessments and remediation recommendations. Integrated infrastructure metadata, deployment history, IAM, EC2, and network context to provide security engineers with a consolidated view of incidents, significantly reducing manual investigation effort and improving Mean Time to Detect (MTTD) and Mean Time to Investigate (MTTI). Implemented secure AI guardrails, ensuring the agent provided advisory recommendations only, with all production remediation actions requiring human approval. Collaborated with DevOps and Security teams to enhance incident response workflows while improving the consistency and accuracy of AI-driven security investigations through structured prompt engineering and automated event correlation.

Designed and managed an enterprise API management platform supporting more than 400 REST APIs across manufacturing, supply chain, eCommerce, and partner integrations. Led the migration from legacy API Gateway infrastructure to Apigee Hybrid, deployed on Azure AKS, while integrating back-end services hosted across AWS and Azure. Improved API security, observability, deployment automation, and developer onboarding.

Designed and implemented an AI-powered business automation platform using n8n, Python, and Large Language Models (Claude/OpenAI) to streamline knowledge-intensive business processes. The platform integrated Google Workspace services, AI agents, and external APIs to automate document processing, meeting preparation, content generation, and workflow orchestration. It leveraged reusable n8n workflows, HTTP integrations, AI prompt engineering, and modular automation components to reduce manual effort while improving operational efficiency and consistency.

The solution included intelligent document analysis, AI-assisted content generation, multi-agent orchestration, Google Workspace automation, and workflow monitoring. It incorporated Gmail, Google Drive, Google Calendar, Google Docs, Google Sheets, and AI services to build end-to-end business automation pipelines capable of generating summaries, extracting actionable insights, and producing business-ready deliverables.

Designed and implemented secure, modular AWS infrastructure for a private healthcare platform handling sensitive patient information and PHI. Led DevOps and cloud-security initiatives covering infrastructure automation, secure middleware integration, database protection, secrets management, observability, and compliance readiness.

I designed and implemented an Airtable-based healthcare subscription and fulfillment management platform to manage patient memberships, recurring subscription boxes, inventory, and shipments. The solution provided a centralized operational system to identify upcoming boxes based on active memberships, allocate inventory, track fulfillment and shipment status, and reduce manual processing. The Airtable data model was structured for future Shopify integration via AirPower, enabling seamless synchronization of customer, product, subscription, order, inventory, and fulfillment data. The solution also incorporated workflow automation and healthcare-focused data handling practices to support a scalable and operationally efficient subscription management process.

Designed and implemented an AI-assisted engineering and DevOps automation framework for enterprise cloud environments. The project combined GitHub Copilot, Claude Code, infrastructure as code, CI/CD, automated testing, security scanning, and human quality gates to improve engineering productivity while maintaining production standards.

Built agentic workflows to analyze existing codebases, generate and review Terraform/Pulumi infrastructure, create CI/CD pipelines, troubleshoot deployment issues, produce technical documentation, and automate repetitive DevOps operations.

Established engineering review criteria covering code quality, architecture, security, testability, maintainability, performance, observability, and deployment readiness. AI-generated changes were validated through automated tests, security checks, pull-request reviews, and controlled release processes before production deployment.

The project also covered real-world engineering workflows, including code comprehension, refactoring, test generation, cloud migration, code review, and performance optimization, providing a structured approach for distinguishing functional code from secure, scalable, maintainable, and production-ready engineering.