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mrmahendra27/README.md

Mahendra Singh Khati

Backend Engineer • Distributed Systems • AI Workflow Platforms

Building scalable backend architectures, event-driven systems, AI-powered workflows, and high-throughput data platforms.


🧠 Engineering Profile

⚙️ Systems

  • Distributed Architectures
  • Event-Driven Workflows
  • Queue Orchestration
  • Async Processing
  • Workflow Reliability
  • Retry Mechanisms

🤖 AI Platforms

  • Amazon Bedrock
  • RAG Pipelines
  • LiteLLM
  • AI Agents
  • Conversational Systems
  • Context Orchestration

📊 Data & APIs

  • High-Throughput APIs
  • ETL Pipelines
  • PostgreSQL Optimization
  • Data Processing
  • Concurrency Patterns
  • Analytics Systems

⚡ Core Stack

Backend & APIs

PHP Python Laravel FastAPI Node.js


Distributed Systems

RabbitMQ AWS SQS AWS EventBridge Redis


AI & Data

Amazon Bedrock LiteLLM PostgreSQL Pandas

🚀 Featured Architectures

⚙️ Event-Driven Workflow Platform

Challenge

Critical operational workflows depended on synchronous execution paths and tightly coupled retry logic, making recurring workflows and delayed retries difficult to manage reliably.

Solution

Designed distributed orchestration pipelines using RabbitMQ, AWS EventBridge, and Amazon SQS to support delayed execution, distributed retries, recurring processing, and fault-isolated workflow stages.

Impact

  • Eliminated blocking retry logic
  • Improved resiliency of asynchronous workflows
  • Reduced duplicate event processing
  • Increased operational reliability under failure conditions

PHP RabbitMQ AWS SQS AWS EventBridge Redis PostgreSQL

🤖 Smart Maintenance Assistant

Challenge

Operational workflows relied on repetitive intake processes and manual triage, creating delays and fragmented context gathering.

Solution

Built an AI-powered conversational workflow platform using Amazon Bedrock, LiteLLM, retrieval pipelines, and asynchronous orchestration workflows for intelligent backend automation.

Impact

  • Reduced manual operational triage
  • Improved contextual data collection
  • Minimized unnecessary escalations
  • Established reliable AI orchestration boundaries

Amazon Bedrock LiteLLM RAG Python AWS SQS PostgreSQL

📊 Distributed Data Intelligence Platform

Challenge

Large-scale ingestion and analytics workflows were bottlenecked by synchronous scraping pipelines and inefficient database access patterns.

Solution

Built concurrent scraping and ETL pipelines using Python, Selenium, Pandas, and PostgreSQL with optimized indexing and scalable analytical APIs.

Impact

  • Increased scraping throughput by 50%
  • Reduced database latency by 35%
  • Improved analytical processing reliability

Python FastAPI PostgreSQL Selenium Pandas Matplotlib

📈 Engineering Interests

Distributed Systems AI Workflow Orchestration Backend Reliability
Event-Driven Architectures Retrieval-Augmented Systems High-Performance APIs
Workflow Automation Scalable Data Platforms Platform Engineering

“Scalable systems are built through explicit architecture, observability, and reliable execution boundaries.”

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