Hello
rohith siliveru
AI&ML STUDENT
I am a fourth-year Computer Science and Engineering student specializing in Artificial Intelligence and Machine Learning. I believe tech growth is driven by consistency, disciplined execution, and solving tangible real-world problems. My technical focus is centered on building end-to-end ML lifecyclesranging from data engineering and predictive modeling to deploying intelligent, scalable systems in production environment frameworks. Beyond code, I actively manage cross-functional initiatives, combining technical architecture with ecosystem leadership.Â
technical skills
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Frameworks & Libraries: PyTorch, TensorFlow, Hugging Face, Scikit-Learn, OpenCV.
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Data & Deployment: Docker, AWS/GCP, FastAPI, Streamlit, PostgreSQL, MLflow.
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Languages: Python, SQL, C++
projects
1. The MLOps Project: End-to-End Automated Pipeline
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The Concept: Create a complete, automated pipeline that trains, evaluates, and deploys a model when new data arrives.
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Why it stands out: Most students only know how to train a model in a Jupyter Notebook. This proves you understand the industry software engineering lifecycle.
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Tech Stack: GitHub Actions (CI/CD), MLflow or DVC (data/model versioning), FastAPI, Docker, and AWS EC2 or Hugging Face Spaces.
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2. The GenAI Project: Retrieval-Augmented Generation (RAG) System
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The Concept: Build a multi-modal RAG system that allows users to chat with complex documents (like financial reports or medical papers) containing both text and charts.
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Why it stands out: RAG is currently one of the most in-demand industry skills. Moving past basic text-only RAG to parse charts/tables shows advanced skills.