Rajan Devkota | Portfolio

01. The Architect

I am a Software Engineer with 3 years of robust experience building scalable backend systems. While my roots are in traditional software development—optimizing SQL queries and deploying microservices—my passion has evolved.

I am currently transitioning into Data Engineering and Machine Learning. I don't just want to write code that executes commands; I want to architect systems that learn, adapt, and derive value from data.

My background in DevOps gives me a unique edge: I understand not just how to build a model, but how to deploy it reliably in a production environment.

Years Experience

10+

Projects Deployed

SQL

Data Mastery

AI Enthusiast

02. Data Simulations

Real-time visualizations of data engineering and machine learning concepts.

ETL Pipeline Flow

              > Ingesting CSV logs...

              > Transforming: Cleaning Nulls...

              > Loading to Data Warehouse.

Model Training (Linear Regression)

Loss: 0.89
Epoch: 12

              > Optimizing weights...

              > Minimizing Mean Squared Error.

03. The Timeline

2023 - Present

Software Engineer (Backend Focused)

Tech Solutions Inc.

Led the migration of legacy monoliths to microservices. Optimized API latency by 40%. Started integrating data pipelines for internal analytics.

2021 - 2023

Junior Software Engineer

Innovate Corp

Developed RESTful APIs using Node.js and Python. Managed PostgreSQL databases and implemented CI/CD pipelines using Jenkins and Docker.

2020 - 2021

Software Developer Intern

Startup Hub

Assisted in frontend and backend bug fixing. Learned the fundamentals of agile development and cloud infrastructure (AWS).

04. The Stack

profile_analysis.py

              # Analyzing proficiency levels...
              class
                Engineer:
              
                def
                __init__(self):
              
                self.backend = ["Python", "Node.js"]
              
                self.data_eng = ["Spark", "Kafka",
                "Airflow"]
              
                self.cloud = ["AWS", "Docker",
                "K8s"]
              
              # Visualizing data stream below...
              print("Stack Loaded Successfully")

Backend Development 90%

Database & SQL 85%

Data Engineering (ETL/Pipelines) 70%

Machine Learning / AI 60%

05. Lab Works

Scalable Microservices

Designed a fault-tolerant payment processing service handling 10k+ requests/minute.

Python Docker Redis

Code

Real-time Data Pipeline

A streaming pipeline that ingests server logs via Kafka, processes them with Spark, and visualizes anomalies.

Kafka Spark ELK