Meshu Deb Nath
Software Engineer in Germany
Overview
About
Servus!
I'm a generalist software engineer based in Germany, where I've found the perfect balance between professional growth and quality of life. After completing my Master's degree in Web Engineering at Technische Universität Chemnitz, I joined Codeculture GmbH, where I contribute to projects serving millions of users across major enterprise clients.
Working in a lean, agile team has given me the opportunity to wear many hats—spanning full-stack development, DevOps, machine learning, and cloud infrastructure. This cross-functional experience was intentional; I believe that understanding the complete software development lifecycle makes you a more effective engineer. Whether it's architecting scalable systems, optimizing workflows, or writing clean, maintainable code, I thrive in environments that value craftsmanship and continuous improvement.
Beyond my day-to-day work, I'm passionate about the intersection of AI/ML and quantitative finance. In my free time, I explore trading strategies using both machine learning models and traditional algorithms—constantly learning, experimenting, and refining my approach.
If you're working on something interesting in AI, ML, or want to exchange ideas, I'd love to connect.
Cheers, Meshu
Social Links
Personal GitHub Contributions
Company GitHub Contributions
Recommendations
Experience
Codeculture GmbH
In my current professional workspace, I'm involved in:
- Machine learning & Deep Learning research and development (Python)
- AWS Lambda development (Golang)
- Google Cloud Services (Run functions + Vertex AI)
- Automation software development / ETL (N8N, Typescript, AWS)
- Mobile app development (Flutter, Ionic)
- Go (Programming Language)
- Terraform
- Python
- Machine Learning
- Deep Learning
- AWS Lambda
- Google Cloud
- Vertex AI
- N8N
- TypeScript
- Flutter
- Ionic
- ETL
PRISMADE LABS
Education
Projects(5)
Building a practical MLOps pipeline for automating ML model training, deployment, and monitoring in production.
Key Components:
- Automated training pipeline with MLflow experiment tracking and model versioning
- Containerized workflows using Docker for consistent environments across development and production
- CI/CD automation with GitHub Actions for continuous training and deployment
- Kubernetes orchestration for scalable model serving with FastAPI endpoints
- Prometheus monitoring for tracking prediction latency, error rates, and model performance
- Reproducible experiments with version-controlled code, data, and model artifacts
- Professional Project
- Python
- MLOps
- Docker
- Kubernetes
- FastAPI
- MLflow
- CI/CD
- GitHub Actions
- Prometheus
- Model Serving
Built scalable serverless data processing pipeline using AWS Lambda and Go for processing millions of DynamoDB records efficiently.
Key Components:
- Go-based Lambda functions for high-performance data processing with fast cold starts
- DynamoDB Streams integration for real-time change capture and event processing
- EventBridge for flexible event routing and scheduled processing workflows
- Terraform for infrastructure-as-code and reproducible deployments across environments
- S3 storage with partitioning and compression for cost-effective long-term data retention
- CloudWatch monitoring and alarms for tracking errors, latency, and system health
- Achieved 60% cost reduction compared to EC2-based approach while improving reliability
- Professional Project
- Go (Golang)
- AWS Lambda
- DynamoDB
- S3
- EventBridge
- Terraform
- CloudWatch
- Event-Driven Architecture
Developed feature-rich cross-platform mobile application using Flutter for iOS and Android platforms.
Key Achievements:
- Built intuitive UI with Flutter widgets following Material Design and Cupertino guidelines
- Implemented efficient state management using Bloc pattern for complex app logic
- Integrated Firebase for authentication, real-time database, and push notifications
- Connected to backend REST APIs with proper error handling and offline support
- Achieved 95% code sharing between iOS and Android platforms
- Delivered smooth 60fps performance across devices with optimized rendering
- Professional Project
- Flutter
- Dart
- Firebase
- REST API
- State Management
- Bloc Pattern
- Push Notifications
- iOS
- Android
Building an automated FAQ generation system from video content using Google Cloud AI services.
Key Components:
- Audio extraction from videos using FFmpeg for format conversion and processing
- Google Speech-to-Text API integration for accurate video transcription with punctuation
- Vertex AI (Gemini Pro) for analyzing transcripts and generating relevant Q&A pairs
- FastAPI service deployed on Cloud Run for scalable video processing workflow
- Background task processing for handling large video files asynchronously
- Cost-effective solution at ~$1.50 per hour of video with 88% FAQ relevance rate
- Professional Project
- Google Cloud
- Vertex AI
- Speech-to-Text
- Gemini Pro
- Cloud Run
- Python
- FastAPI
- FFmpeg
Stack
- Python
- TypeScript
- JavaScript
- Go
- Java
- C++
- Kotlin
- Dart
- React
- Angular
- Vue.js
- Node.js
- Flutter
- jQuery
- Strapi
- WordPress
- AWS
- Google Cloud Platform
- AWS Lambda
- Docker
- Kubernetes
- Terraform
- Jenkins
- GitHub Actions
- TeamCity
- Linux
- PostgreSQL
- MySQL
- DynamoDB
- MongoDB
- GraphQL
- Apollo GraphQL
- REST API
- RabbitMQ
- Elasticsearch
- Scikit-learn
- Pandas
- NumPy
- Matplotlib
- Git
- HTML5
- CSS3
- XML
- Mocha
- Scrum
- Agile Methodologies
Certifications(4)
Honors & Awards(2)
Languages(2)
English
- Proficiency
- Fluent
German
- Proficiency
- Intermediate
