I’m a Machine Learning Engineer with 4+ years of experience building real-world AI solutions—especially in Natural Language Processing using Large Language Models.
My recent work includes developing an automatic AI note-taking that support multi-tenancy, a multi-tenant RAG chatbot and a dynamic AI assistant for a Telehealth startup. These solutions allowed multiple organizations to deploy secure, context-aware chat experiences powered by GPT, Pinecone, and PostgreSQL—delivered through scalable APIs built with FASTAPI and hosted on google GCP.
I focus on more than just models—I care about product thinking, user experience, and building systems that solve real problems. My backend skills enable me to take ML projects from concept to deployment, ensuring they’re usable by real teams via well-designed APIs.
I'm especially interested in companies that apply LLMs and AI automation to real-world tasks across industries, and I'm always open to roles that combine research, engineering, and product development. I also have a growing interest in computer vision and cross-modal AI systems.
Let’s build tools that do real work.
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Sure, here's a categorized list of tools commonly used by ML engineers:
Programming Languages:
- Python
Machine Learning Frameworks:
- TensorFlow
- Scikit-learn
Development Environment:
- Jupyter Notebook
- Google Colab
- Visual Studio Code
Data Manipulation and Analysis:
- Pandas
- NumPy
Containerization and Orchestration:
- Docker
Version Control:
- Git
Cloud Platforms:
- AWS
- Azure
- Google Cloud Platform
Big Data Processing:
- Apache Spark
Visualization:
- Matplotlib
- Seaborn
Web Frameworks:
- FastAPI
- Flask
Machine Learning Lifecycle Management:
- MLflow