You will get Data Engineering and ETL using Python
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Project details
Hello! I am a computer engineer by profession.
I have four years of experience in the domain of Data Engineering:
Following are my areas of expertise:
ETL Pipelines
Data warehousing (Amazon Redshift, Big Query)
AWS Glue
AWS Serverless Architecture like Lambda, ECS, Dynamodb
Databases (Mysql, PostgreSQL, Mongodb)
I assure timely delivery and fulfilling the requirements as described by the buyer! I expect to hear from you soon!
I have four years of experience in the domain of Data Engineering:
Following are my areas of expertise:
ETL Pipelines
Data warehousing (Amazon Redshift, Big Query)
AWS Glue
AWS Serverless Architecture like Lambda, ECS, Dynamodb
Databases (Mysql, PostgreSQL, Mongodb)
I assure timely delivery and fulfilling the requirements as described by the buyer! I expect to hear from you soon!
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$500
|
Standard
$2,000
|
Advanced
$5,000
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 15 days |
Number of Revisions | 0 | 0 | 0 |
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About Muhammad Tahir
AI Agent & Full-Stack Engineer | LLM | AWS | Django | Python
100%
Job Success
Karachi, Pakistan - 2:14 am local time
In recent years, I have specialized in architecting **LLM-powered systems and AI agent infrastructures** — building reliable, structured, and controllable AI workflows that integrate seamlessly into enterprise environments.
I focus on systems that are not experimental prototypes, but robust, auditable, and production-ready.
💬 Fluent English communication
🤝 Strong cross-functional collaboration
🏗️ Architecture-first mindset
---
# 🚀 Core Expertise
## 🤖 AI Agent Systems & LLM Infrastructure
• Enterprise-grade AI agent architecture
• Multi-agent orchestration & workflow design
• LangChain / LlamaIndex implementations
• Structured output enforcement (schema-validated JSON pipelines)
• Tool-calling & function-calling systems
• Retrieval-Augmented Generation (RAG) architectures
• Vector databases & semantic search systems
• LLM evaluation, validation & guardrails
• AI compliance & rule-based validation layers
• Context management & memory systems
• Deterministic prompt engineering for production use
• Secure integration with internal APIs
• AI workflow observability & monitoring
---
## 🏗️ Backend & Distributed Systems
• Django / FastAPI / Flask
• Node.js / Express
• REST / GraphQL APIs
• Microservices architecture
• Scalable database design (PostgreSQL, MySQL, MongoDB)
• Elasticsearch
• Authentication & role-based access control
• High-availability system design
---
## 📊 Data Engineering
• Data warehouse architecture
• BigQuery / Redshift
• ETL automation (Airflow)
• Data ingestion & transformation pipelines
• Query performance optimization
• Analytics infrastructure
---
## ☁️ Cloud & Deployment
• AWS (EC2, Lambda, S3, RDS)
• Google Cloud Platform
• Serverless architectures
• Dockerized deployments
• CI/CD pipelines
• Secure production infrastructure
---
## 🎯 What I Prioritize
• Clean, modular architecture
• Long-term maintainability
• Performance & scalability
• Clear documentation
• Responsible and controlled AI system design
If you're building AI-enabled enterprise software and require structured, production-grade implementation — I can help design and deliver it.
Steps for completing your project
After purchasing the project, send requirements so Muhammad Tahir can start the project.
Delivery time starts when Muhammad Tahir receives requirements from you.
Muhammad Tahir works on your project following the steps below.
Revisions may occur after the delivery date.
Create requirements document.
Please prepare a requirements document with the details of Data sources and targets.
Development
Build pipeline using Python and Lambda functions to store data to Data Warehouse.