SYSTEM ARCHITECTURE DIAGRAM // HITENSAM.DEV

Backend Developer Building Resilient Cloud Systems

As a Backend Developer and Cloud Engineer based in India / Remote, I specialize in building highly scalable systems, architecting microservices and high-throughput ETL data pipelines on Microsoft Azure, with zero-downtime reliability powered by CI/CD automation.

[ABOUT_SYSTEM_ARCHITECT]

About Hiten Samalia

Hiten Samalia is a backend and cloud engineer based in India, focused on microservices, Microsoft Azure, ETL data pipelines, CI/CD automation, and resilient production systems. He is open to backend and cloud engineering opportunities in India and remote teams.

[SYS_SUMMARY // SYSTEM SPECIFICATIONS & CORE OBJECTIVE]
EXECUTIVE TECHNICAL SPECIFICATIONSTATUS: HIGH AVAILABILITY

Backend & cloud systems engineer with 2+ years of experience building scalable, cloud-native systems on Microsoft Azure. Experienced in designing high-throughput ETL data ingestion pipelines, asynchronous microservices, and robust CI/CD automation (-70% deployment latency). Seeking high-impact opportunities where engineering rigor, clean system design, and zero-downtime reliability matter.

CORE COMPETENCY 01ETL & MARKET DATAIngestion, transformation, validation & delivery pipelines for downstream fintech systems.
CORE COMPETENCY 02CLOUD RESILIENCEMulti-region Azure failover, disaster recovery, KEDA auto-scaling, and Bicep IaC.
CORE COMPETENCY 03FASTAPI MICROSERVICESAsync REST API endpoints, Pydantic data validation, and SQLAlchemy database connection pooling.
RUNTIME ENVIRONMENT|v2026.07
PRIMARY TARGET:Backend & Cloud Developer
CURRENT STATUS:OPEN TO OPPORTUNITIES
DEPLOYMENT REGION:India / Global Remote
EXPERIENCE LEVEL:2+ Years Enterprise Prod
DEGREE CREDENTIAL:B.Tech CS (8.26 CGPA)
INTERACTIVE RESUME PIPELINE
[ETL_PROD_WORKERS // CORE_EXECUTION_NODE]COMPLETED // 2+ YEARS EXP

EXPERIENCE & DEPLOYMENT TIMELINE

Zversal Private Limited (Backend & Cloud) + Coding Ninjas

THROUGHPUT / KEY METRIC70% Deploy Acceleration | Multi-Region Azure DR
INSPECTED DATA PACKETS (4 ITEMS)JSON / SCHEMA VERIFIED
1.QuoddFunds ETL Pipelines: Ingesting & validating mutual fund market data for QUODD (US Client)
2.CI/CD Automation: Built GitHub Actions reducing rollout latency by 70% with zero downtime
3.Disaster Recovery: Automated multi-region Azure failover reducing MTTR significantly
4.Mentorship: Resolved 300+ Java/DSA architecture tickets with 4.68/5 student rating
TELEMETRY CONSOLE // REAL-TIME TRACESTATUS: 200 OK

[UTC LIVE_STREAM][STREAM ACTIVE] Processing financial data transformations across distributed Azure microservices. Disaster Recovery heartbeat verified: SYNC_OK across Central India & South India regions.

DEPLOYMENT VELOCITY-70% BUILD TIMEGitHub Actions CI/CD Rollouts
DISASTER RECOVERYMULTI-REGION DRAutomated Azure Failover
BACKEND MICROSERVICESASYNC REST APIsFastAPI & SQLAlchemy ORM
TECHNICAL LEADERSHIP300+ TICKETS RESOLVED4.68/5 Student Rating @ Coding Ninjas
[DEPLOY_LOG // PRODUCTION ENVIRONMENT EXECUTION TIMELINE]

Backend and Cloud Engineering Experience

2+ YEARS PROD CONTINUITY|2 VERIFIED WORKLOADS
1
ZVERSAL PRIVATE LIMITED|Jan 2024 -- Completed|Mohali, Punjab

Full Stack Developer(Internship + Full Time)

CLIENT PARTNERSHIP: QUODD (US-Based Financial Market Data Client)

STATUS: COMPLETED
CI/CD DEPLOY TIME-70% reduction
FAILOVER CONFIGMulti-Region DR
RELIABILITYZero-Downtime Rollouts
[DEPLOYED TECH STACK & SYSTEM TOOLS]
.NET (C#)PythonAzure Function AppsAzure Container AppsGitHub Actions CI/CDBicep IaCSQL / ETL Pipelines
Problem / Objective

Collaborated with a US-based client (QUODD) to design, build, and maintain 7+ Azure-based microservices exposing RESTful APIs for the QuoddFunds platform, with a focus on backend architecture, performance, and scalability.

Role / Contribution

Contributed to the QuoddFunds platform by building CI/CD pipelines with GitHub Actions and designing automated multi-region Disaster Recovery (DR) pipelines.

Technologies Used

Azure Function Apps, Azure Container Apps, GitHub Actions CI/CD, Bicep IaC, SQL, ETL Pipelines

Outcome / Impact

Achieved a 70% reduction in deployment time, zero-downtime rollouts, and ensured data consistency and reliability across downstream systems with multi-region failover.

ARCHITECTURE TRACE // QUODDFUNDS ETL & DR PIPELINESLA VERIFIED

[00:01:12] INGESTION: Mutual fund tick streams ingested via Azure Function triggers.

[00:01:14] TRANSFORMATION: Validating structured schemas across .NET / Python worker pools.

[00:01:15] CI/CD ACCELERATION: GitHub Actions workflow deployed with zero downtime (70% faster feedback loop).

[00:01:18] DISASTER RECOVERY: Automated multi-region Azure failover heartbeat active and operational.

2
CODING NINJAS|Aug 2023 -- Nov 2023|Remote

Teaching Assistant -- Java & DSA(Part-Time Academic Role)

STATUS: COMPLETED
DOUBTS RESOLVED300+ Tickets
STUDENT RATING4.68 / 5.0 SLA
DOMAINSCore Java & OOP
[SERVICE_REGISTRY // ACTIVE CLOUD WORKLOADS & RAG AGENTS]

Python, FastAPI, and Azure Projects

FASTAPI • LANGGRAPH • AZURE SWA|ALL WORKLOADS HEALTHY
Backend API|STATUS: DEPLOYED

SALES LEADERBOARD API — FREELANCE (ITC SALT PRODUCTS)

Serverless leaderboard system for an ITC field sales team of 173+ people.

SCALE173+ Field Reps
DATA STOREGoogle Sheets
DEPLOYMENTAzure Functions
[SYSTEM COMPONENT STACK]
PythonFastAPIGoogle Sheets APIAzure Function Apps
Problem / Objective

Built and deployed a sales leaderboard system for an ITC field sales team of 173+ people across 11 locations, used daily to rank reps by quantity sold within their location and tier and drive competition.

Role / Contribution

Used Google Sheets as a lightweight data store for rep entries, exposed through FastAPI endpoints deployed serverless on Azure Function Apps; partnered with a frontend developer to ship a complete, production-usable dashboard.

Technologies Used

Python, FastAPI, Google Sheets API, Azure Function Apps

Outcome / Impact

Shipped a complete, production-usable dashboard driving daily competition among field sales reps.

ARCHITECTURE BREAKDOWN

PATTERN: Serverless FastAPI Endpoints with Google Sheets as Lightweight DB

THROUGHPUT/LATENCY: Daily concurrent usage by 173+ field reps

CORE INNOVATION: Leveraging Google Sheets API for a cost-effective, easily accessible backend store for non-technical stakeholders.

Web App & Microservices Platform|STATUS: DEPLOYED

INVENTORY MANAGEMENT SYSTEM

Full-stack web application for sales/inventory monitoring with FastAPI microservices and MCP integration.

INVENTORY EFFICIENCY+35% Operational Gain
ARCHITECTURE MIGRATIONDjango MVC -> FastAPI
DEPLOYMENT HOSTINGAzure Static Web Apps
[SYSTEM COMPONENT STACK]
PythonFastAPISQLAlchemyDjango LegacyAzure Static Web AppsMCP Protocol
Problem / Objective

Develop a full-stack web application for monitoring sales and inventory operations with iterative feature enhancements.

Role / Contribution

Migrated the backend from Django MVC to a high-throughput FastAPI microservices architecture with SQLAlchemy ORM and MCP integration.

Technologies Used

Python, FastAPI, SQLAlchemy, Azure Static Web Apps, MCP Protocol

Outcome / Impact

Improved inventory control efficiency by 35% through optimized tracking and reporting pipelines.

ARCHITECTURE BREAKDOWN

PATTERN: Asynchronous REST Microservices with Model Context Protocol (MCP) Bridge

THROUGHPUT/LATENCY: 35% efficiency boost through automated inventory alert pipelines

CORE INNOVATION: Decoupled frontend static hosting (Azure SWA) from backend async microservice worker nodes.

Backend System / Agentic AI|STATUS: POC

AI-POWERED WHATSAPP RAG CHATBOT

Multi-tenant RAG chatbot delivering real-time product recommendations via vector search & tiered tool authorization.

HALLUCINATION CONTROLTier-Scoped Tool Guardrails
VECTOR ENGINEPinecone + Jina AI Embeddings
INFERENCE SPEEDCerebras Ultra-Low Latency
[SYSTEM COMPONENT STACK]
PythonFastAPILangGraphPineconeJina AICerebras API
Problem / Objective

Build a production-grade multi-tenant RAG chatbot delivering real-time product recommendations through semantic vector search.

Role / Contribution

Engineered the vector search knowledge base and architected a dynamic LangGraph agent with subscription-tier-based tool access.

Technologies Used

Python, FastAPI, LangGraph, Pinecone, Jina AI, Cerebras API

Outcome / Impact

Eliminated unauthorized tool hallucination by ensuring clients interact only with capabilities scoped to their tier.

ARCHITECTURE BREAKDOWN

PATTERN: Stateful Agent Workflow (LangGraph) with Multi-Tenant Vector Partitioning

THROUGHPUT/LATENCY: Sub-400ms vector retrieval + Cerebras token generation

CORE INNOVATION: Tiered MCP tool registry preventing lower-tier clients from executing privileged backend mutations.

IoT Backend Platform|STATUS: IN PROGRESS // ACTIVE

WIRELESS PRINT & WEIGH SYSTEM — IOT PLATFORM

Multi-agent IoT backend platform that makes existing printers wireless and auto-populates labels with live scale data.

DEPLOYMENTEmbedded Linux
ARCHITECTUREMulti-Agent Services
RELIABILITYAtomic State Persistence
[SYSTEM COMPONENT STACK]
PythonFastAPIEmbedded LinuxReact
Problem / Objective

Architected a multi-agent Python/FastAPI backend (separate print, network, and weighing-scale agent services) that makes existing printers wireless and auto-populates labels with live scale data, currently deployed for jewellery retail label printing.

Role / Contribution

Hardened the embedded Linux deployment for rugged, power-loss-prone environments using a read-only root filesystem with atomic state persistence to prevent data corruption, and built a companion React-based label designer UI.

Technologies Used

Python, FastAPI, Embedded Linux, React

Outcome / Impact

Currently migrating parts of the architecture to a hybrid cloud/microcontroller design for cost efficiency.

ARCHITECTURE BREAKDOWN

PATTERN: Multi-Agent Edge Microservices with Read-Only Root FS

THROUGHPUT/LATENCY: Real-time weighing scale data ingestion and wireless print execution

CORE INNOVATION: Atomic state persistence on embedded Linux to prevent data corruption during unexpected power losses.

[STACK_DIAGRAM // SYSTEM COMPONENTS & TECHNICAL COMPETENCIES]

Technical Skills

GROUPED BY SCHEMATIC MODULE|PRODUCTION VERIFIED
[MOD_01_LANGUAGES]2 MODULES

PROGRAMMING

Core general-purpose runtime languages for system engineering and backend services.

PythonCore Production Stack

PROD USE CASE: Async FastAPI services, LangGraph orchestration, ETL data pipelines

.NET (C#)Core Production Stack

PROD USE CASE: High-performance enterprise microservices on Azure container runtime

DEPENDENCIES RESOLVEDSLA: CONTINUOUS VERIFICATION
[MOD_02_FRAMEWORKS]3 MODULES

FRAMEWORKS & LIBRARIES

Backend execution frameworks, ORMs, and stateful agentic AI orchestration.

FastAPICore Production Stack

PROD USE CASE: Asynchronous REST endpoints, OpenAPI schemas, Pydantic type validation

LangGraphCore Production Stack

PROD USE CASE: Cyclic multi-agent workflows, subscription-based tool routing, state persistence

SQLAlchemyCore Production Stack

PROD USE CASE: Relational database connection pooling, async ORM queries, migration tracking

DEPENDENCIES RESOLVEDSLA: CONTINUOUS VERIFICATION
[MOD_03_DEVOPS_IAC]5 MODULES

DEVOPS & TOOLS

Automated deployment pipelines, infrastructure as code, and containerization engines.

GitHub Actions CI/CDCore Production Stack

PROD USE CASE: 70% faster build & zero-downtime rollout workflows, automated testing

DockerCore Production Stack

PROD USE CASE: Multi-stage container builds, reproducible microservice runtime packaging

Bicep (IaC)Core Production Stack

PROD USE CASE: Declarative Azure resource provisioning, parameter-driven environment cloning

GitDaily Proficiency

PROD USE CASE: Branching strategies, atomic commits, submodule and repository orchestration

Linux AdministrationDaily Proficiency

PROD USE CASE: Server provisioning, bash scripting, systemd service diagnostics, cron automation

DEPENDENCIES RESOLVEDSLA: CONTINUOUS VERIFICATION
[MOD_04_CLOUD_NATIVE]4 MODULES

CLOUD (AZURE)

Microsoft Azure distributed compute, networking, and serverless architectures.

Azure Function AppsCore Production Stack

PROD USE CASE: Event-driven serverless ingestion triggers for mutual fund ETL pipelines

Azure Container AppsCore Production Stack

PROD USE CASE: Managed Kubernetes environment with automated KEDA scale-to-zero

Azure Web AppsCore Production Stack

PROD USE CASE: Production API hosting with custom domain binding and SSL offloading

Azure Virtual NetworkAdvanced Experience

PROD USE CASE: VNet peering, private endpoints, subnet isolation, disaster recovery routing

DEPENDENCIES RESOLVEDSLA: CONTINUOUS VERIFICATION
[CERT_STORE & ACADEMIC_CREDENTIALS // CRYPTOGRAPHIC VERIFICATION]

Certifications and Education

5 VERIFIED CERTIFICATES|B.TECH CS (8.26 CGPA)
VERIFIED CERTIFICATE STORE (5 ENTRIES)STATUS: ISSUED & VALID
2025VERIFIED

Microsoft: Azure Fundamentals

Microsoft Certified

ID: MS-AZ900-VERIFIED-2025CLOUD_INFRA
2023VERIFIED

Python for Data Science

NPTEL

ID: NPTEL-PYDATA-CREDENTIALDATA_AI
2023VERIFIED

Design Thinking – A Primer

NPTEL

ID: NPTEL-DESIGN-THINKINGCORE_CS
2023VERIFIED

Data Structures in Java

Coding Ninjas

ID: CN-JAVA-DSA-EXCELLENCECORE_CS
2023VERIFIED

Introduction to Java

Coding Ninjas

ID: CN-INTRO-JAVA-HONORSCORE_CS
ACADEMIC PIPELINE NODESDEGREE VERIFIED
[ACADEMIC_NODE_01 // PIET_CS_DEGREE]Panipat, Haryana

Panipat Institute of Engineering and Technology

B.Tech in Computer Science and Engineering

Graduated June 20248.26 / 10.0 CGPA
[ACADEMIC_NODE_02 // BBPS_DELHI]Pitampura, Delhi

Bal Bharati Public School

High School Certification / PCM Focus

October 2020
FOUNDATIONAL METRICS SUMMARY

Hiten graduated with an exceptional 8.26 / 10.0 CGPA from PIET while simultaneously serving as a Java & DSA Teaching Assistant at Coding Ninjas, debugging algorithms and resolving over 300+ architecture tickets.

[AI_ENGINE_QUERY_INTERFACE]

Frequently Asked Questions

What does Hiten Samalia specialize in?

Hiten specializes in building scalable backend architectures, high-throughput ETL data pipelines, and async Python microservices (FastAPI) on Microsoft Azure.

What technologies does Hiten use?

He primarily uses Python, FastAPI, SQLAlchemy, and LangGraph for backend engineering. For cloud and DevOps, he leverages Microsoft Azure (Function Apps, Container Apps, Static Web Apps), Docker, GitHub Actions, and Bicep IaC.

What kind of roles is Hiten open to?

He is currently OPEN TO BACKEND & CLOUD ROLES, particularly positions focused on backend engineering, data pipelines, cloud architecture, and building resilient systems.

Where is Hiten based?

Hiten is based in India and is open to remote roles or teams operating in/from India.