Software Developer transitioning into AI & Machine Learning

I build software, and now I'm learning how to make it intelligent.

My background includes web development, mobile development, IT support, and software engineering, and I’m currently focused on strengthening my foundations in:

• Python
• Machine Learning
• Deep Learning
• LLMs & RAG systems
• AI-focused backend development

I’m especially interested in building projects that help me understand systems deeply — not just use libraries, but learn how things work under the hood.

Right now, I’m working on:

• implementing core ML algorithms from scratch
• learning deep learning training workflows
• building practical AI projects with clean backend architecture
• improving my GitHub portfolio for AI/ML internship and junior AI roles

Projects focused on understanding ML fundamentals by implementing algorithms manually and analyzing their behavior.

• K-Nearest Neighbors (KNN) from Scratch

  • implemented with NumPy
  • evaluated on the Digits dataset
  • explored performance across different values of K
  • included confusion matrix and misclassification analysis

• Logistic Regression from Scratch with L2 Regularization

  • implemented sigmoid, binary cross-entropy loss, and gradient descent manually
  • explored the effect of regularization strength
  • evaluated using accuracy, precision, recall, and F1-score

Projects focused on practical AI application development and system design.

• SmartDocs AI (RAG-based document QA assistant)
  • FastAPI-based backend for question answering over uploaded documents
  • document parsing, chunking, embeddings, vector search, and answer generation
  • designed to learn retrieval-augmented generation (RAG) architecture end to end

Projects focused on understanding training mechanics, monitoring, and optimization.

• Deep Learning Training Lab (in progress)
  • learning backpropagation and gradient flow through simple computational examples
  • training neural networks with TensorFlow/Keras
  • using callbacks, learning rate scheduling, and TensorBoard
  • focused on building both conceptual understanding and practical workflow

Other Background

• Web Development
• Mobile Development
• IT Support
• Software Engineering Fundamentals

I’m currently building my understanding of:

• machine learning fundamentals
• deep learning and training workflows
• backpropagation and optimization
• LLM application development
• retrieval-augmented generation (RAG)
• AI system design and backend integration

I’m currently looking for opportunities where I can grow as:

• AI/ML Intern
• Junior AI Engineer
• Python / AI Backend Intern
• Machine Learning Intern

I’m especially interested in roles where I can keep learning while contributing through clean implementation, problem solving, and steady technical growth.

I care a lot about:

• understanding concepts deeply
• writing clear and organized code
• building projects that are both educational and practical
• improving step by step with consistency

I’m building my way into AI through real projects, strong fundamentals, and consistent practice.