Software Engineer transitioning into Data Science & AI Engineering
Bridging traditional software engineering excellence with modern AI/ML workflows
- 💼 Starting as Full Stack Developer in a Data Science Team at Crowe Foederer (September 2025)
- 🤖 Building production-ready AI agents with LangChain & Model Context Protocol (MCP)
- 🧱 Bringing Clean Architecture, CQRS, and DDD principles to ML workflows
- 📊 Passionate about scalable systems, agent orchestration, and AI-powered applications
- 🔬 Exploring the intersection of software engineering best practices and data science
Backend: .NET Core, EF Core, MediatR, AutoMapper, FluentValidation, Ardalis
Frontend: Angular 19, RxJS, PrimeNG
Architecture: Domain-Driven Design, CQRS, Clean Architecture
DevOps: GitHub Actions, Docker
Testing: xUnit, Jest
Core Stack: Python, FastAPI, async/await, Pydantic
AI/ML: LangChain, LangGraph, Model Context Protocol (MCP)
Data: Vector databases, RAG implementations, ML pipelines
Frontend: React (hooks, state management)
- 🌦️ WeatherAI - AI-integrated Weather API with intelligent dashboard
- 🤖 CodeAssistant - LLM-powered code explainer with refactoring capabilities
- 📊 Productivity Tracker Backend - FastAPI backend with auth, RBAC, and comprehensive tooling (Latest)
- 💬 Studdit 2.0 - Q&A platform with voting system (Clean Architecture, JWT)
- 🎉 MeetMe 2.0 - Event hosting platform with real-time features
- 📱 Mobile Employee App 2.0 - Cross-platform Ionic + Angular app
- 🔌 RESTful CRUD API - Clean architecture with comprehensive testing
- 🎨 Design Patterns C# - Real-world pattern implementations
- 🧮 Algorithms & Data Structures - CS fundamentals with unit tests
Modernizing legacy systems while mastering AI/ML engineering
I'm rebuilding all legacy projects (prefixed with --Legacy--) using:
- ✅ Modern architecture patterns (Clean Architecture, CQRS, DDD)
- ✅ Comprehensive testing strategies (TDD, integration tests)
- ✅ AI/ML integrations (LangChain agents, MCP, RAG)
- ✅ Production-ready tooling (pre-commit hooks, CI/CD, type safety)
Goal: Bring software engineering excellence to data science workflows and build intelligent, maintainable systems.
Q4 2025: Mastering FastAPI, Python async, LangChain basics
*Q1 20256: Advanced agent orchestration, MCP, React modernization
Q2 2026+: Production AI systems, ML pipelines, vector databases, agent-based architectures
💡 "Building the bridge between traditional software engineering and AI-powered systems"