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CLOUD ARCHITECTURE 

MY PASSION FOR CLOUD 

Having a wild imagination and traits of perfectionism, I had struggled with finding correct resources and time to bring my imaginations to life. Comes the cloud platforms, Google Cloud Platform and Amazon Web Services Platform, getting access to a wide variety of tools abled me to develop tools and with ease.

Cloud Platform is the world at your fingertips

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Cloud Architecture is the blueprint of any tool developed in cloud. It includes several key components and multiple sub tools offered by cloud platforms for specific uses. A properly built architecture is valuable for productivity gains, less costly to maintain and less time consuming. Interact with the following App to understand critical components and my favorite tools being used for my projects.

Click on the main circles.

Projects Portfolio

My Projects Portfolio include the most recent projects that I have worked with and been a part of. Feel free to interact and get to know more about the projects. The projects include the project architecture, tools being used, projects details, skills and certifications acquired, my main responsibilities. Some details for some projects are classified under NDAs hence can not be revealed. 

Cloud Projects​​

Automated Org Chart Builder for AMARIS

Purpose of the project was to Enable AMARIS consultant managers to ingest static team-structure images for third-party clients, enrich personnel records, and maintain a live, exportable organization chart. As only pictures of client team structures were present and lacked an automated way to capture roles, contact details, LinkedIn profiles, and bios.
High-Level Outcome: Reduced manual org-chart creation time from 2 months to under 7 days and improved data-update cadence to near real time.

Business Objectives

Accelerate go-to-market for org-chart deliverables

Ensure data freshness with continuous updates

Enrich basic hierarchies with complete contact metadata

Achieve 95%+ data extraction accuracy

Automate end-to-end processing within a 7-day sprint

Securely manage sensitive personal data

Solution Design

Data Flow & Control Flow:

  • User selects company & uploads org-chart image via UI

  • UI stores image in Google Cloud Storage

  • “DocProc” fetches image, calls the custom Document AI processor, extracts hierarchy (Manager, Department, Team Members)

  • Processor writes raw hierarchy into BigQuery

  • “EnrichProc” retrieves new rows, queries Lusha API for emails, phones, LinkedIn, bios, and updates BigQuery

  • Looker Studio renders the hierarchical chart; users export to PDF/Excel

Core Components:

  • UI front-end for image upload (React)

  • Google Cloud Storage bucket (raw images)

  • Cloud Run service “DocProc” invoking custom Document AI model

  • BigQuery dataset “org_charts”

  • Cloud Run service “EnrichProc” calling Lusha API

  • Looker Studio dashboard with live BigQuery connection

Scaling & Compliance:

  • Cloud Run autoscaling (min-instances=1, max-instances=10)

  • Multi-region BigQuery with default replication

  • IAM roles:

    • UI service account with “storage.objectViewer”

    • DocProc service account with “documentai.process” & “bigquery.dataEditor”

    • EnrichProc service account with “bigquery.dataEditor”

  • VPC Service Controls around Storage & BigQuery

  • Cloud Armor firewall rules on all ingress

  • Data encrypted at rest and in transit

Results

CV Skills Analyzer

Streamline AMARIS recruitment process through automated CV analysis, skills extraction, and intelligent candidate-job matching to eliminate manual screening bottlenecks.
High-Level Outcome: Reduced candidate screening time from 2 weeks to instant while improving match accuracy by 85% and establishing a scalable recruitment intelligence platform.

Accelerate time-to-hire by eliminating manual CV review processes

Improve candidate-job matching precision through standardized skills analysis

Establish data-driven recruitment insights and reporting capabilities

Achieve 95%+ accuracy in CV data extraction and skills identification

Process 500 24 hours

Implement real-time candidate ranking with weighted suitability scores

Build secure, compliant data handling for sensitive candidate information

Business Objectives

Solution Design

Core Components:

  • React-based UI for consultant managers

  • Cloud Run "CVProcessor" service with custom Document AI model

  • Cloud Run "SkillsExtractor" integrating Lightcast API

  • Cloud Run "JobAnalyzer" for job requirement processing

  • Custom Vertex AI Gemini model for candidate matching

  • BigQuery data warehouse with structured candidate/job datasets

  • Looker Studio dashboard for recruitment analytics

Data Flow & Control Flow:

  • Manager enters Job ID into UI

  • "CVProcessor" calls Mantu ATS API to retrieve all candidate CVs for position

  • Document AI OCR extracts structured data (name, email, mobile, experience, languages)

  • "SkillsExtractor" sends CV text to Lightcast API for skills identification and normalization

  • Skills data stored in BigQuery with nested JSON structure

  • "JobAnalyzer" fetches job description from Mantu database via API

  • Job requirements processed through Lightcast for required skills taxonomy

  • Custom Gemini model analyzes candidate-job fit using proprietary framework

  • Weighted suitability rankings generated based on must-have/hard/soft skills matching

  • UI displays ranked candidates with detailed skills breakdown and scores

Scaling & Compliance:

  • Cloud Run auto-scaling (min-instances=2, max-instances=50 per service)

  • BigQuery multi-region replication with 99.9% SLA

  • Circuit breaker patterns for external API dependencies

  • IAM roles:

    • UI service account: mantu.ats.reader, bigquery.dataViewer

    • CVProcessor: documentai.process, bigquery.dataEditor

    • SkillsExtractor: bigquery.dataEditor, external API access

    • JobAnalyzer: bigquery.dataEditor, Mantu API access

    • Gemini service: aiplatform.user, bigquery.dataViewer

  • VPC Service Controls around BigQuery and sensitive data stores

  • Cloud Armor security policies for API protection

  • Data encryption at rest and in transit with customer-managed keys

Results

Automated Social Media Contest Winner Selection System

Streamline Freedom Confectionery's social media contest management by automating the tracking of Instagram follows, hashtag usage, and Google reviews to eliminate manual winner selection processes
High-Level Outcome: Transformed a 4-day manual process into a 1-day automated system, reducing marketing team workload by 80% while maintaining compliance with privacy regulations and NDAs

Business Objectives

Accelerate contest completion cycles to maintain social media engagement momentum

Reduce manual verification workload for marketing team members

Ensure fair and transparent winner selection through automated processes

Scale contest frequency without proportional increase in human resources

Achieve 75%+ accuracy in cross-platform participant identification

Process contest entries within 24 hours of challenge completion

Maintain data privacy compliance while handling social media information

Build scalable architecture supporting multiple concurrent contests

Solution Design

Core Components:

  • Web-based UI for contest management and winner announcement

  • BigQuery data warehouse with separated contestant action databases

  • Custom Vertex AI Gemini model for cross-platform identity matching

  • Cloud Run "InstagramProcessor" service utilizing Instagram REST API

  • Cloud Run "ReviewsCollector" integrating Google Reviews API

  • Cloud Run "WinnerSelector" for automated contest result generation

  • Looker Studio dashboard for analytics

Data Flow & Control Flow:

  • Due to NDA, the Data Flow and Control Flow can not be revealed

Scaling & Compliance:

  • Cloud Run auto-scaling (min-instances=1, max-instances=20 per service)

  • BigQuery partitioned tables by contest date for performance optimization

  • Circuit breaker patterns for Instagram and Google API dependencies

  • IAM roles with principle:​

  • VPC Service Controls around BigQuery and sensitive data stores

  • Cloud Armor security policies for API protection

  • Data retention policies compliant with privacy regulations

Results

MY EXPERTISE TOOLS

Following are the tools and skill areas I am specialized in. However, these don't limit me.

Data Analysis

Data Visualization

Data Analytics

R

Data Cleaning

SQL

API Dev.

Web Services

Programming

Containerization

Cloud Computing

Google BigQuery

Kubernetes

GCP

Terraform

AWS

AZURE

Docker

Cloud Architecture

Python

Java Script

JSON

DevOps

PowerShell

Cloud Automation

IAM

Vertex AI

Document AI

Gemini AI

VPC

Amazon S3

Virtual Machines

Azure Storage

CI/CD Pipeline

GKE

Jenkins

ELK Stack

Google Cloud Logging

RBAC

ABAC

MFA

SSO

GDPR

CCPA

DNS Configurations

Google Cloud SQL

Firestone

YAML

AGILE

Project Management

SCRUM

JIRA

MIRO

Trello

Machine Learning

MLOps

Edge Computing

Cost Optimization

MY CERTIFICATIONS

Click the floating icons to get to know more in details of the certifications

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