Machine Learning Engineer focused on NLP and LLM systems, experienced in building RAG pipelines, vector search systems, and multi-agent AI workflows using Python, Transformers, and MLOps tools.
B.Tech in Electrical & Electronics Engineering, JSS Academy of Technical Education — Graduation Year: 2026. Academic focus on Machine Learning, NLP, and AI systems, with projects involving RAG pipelines, vector databases, and LLM-based architectures.
Python, SQL, NLP, LLMs, Transformers, RAG, Supervised & Unsupervised Learning, TensorFlow, Scikit-learn, Pandas, NumPy, Hugging Face, LangChain, Flask, Data Analysis, EDA, Statistical Analysis, Data Visualization, Docker, Kubernetes, CI/CD, GitHub Actions, Qdrant, Linux, AWS, GCP, Git, Jupyter Notebook, VS Code
AI platform for research paper discovery, research gap detection, and contextual Q&A. It converts papers into embeddings using Sentence-Transformers, stores them in Qdrant for semantic search, detects potential research gaps using HDBSCAN clustering, and answers questions through a RAG pipeline. It also auto-generates research presentations from extracted insights.
Automated CI/CD code review system that analyzes GitHub pull requests using a local LLM. An n8n workflow captures PR diffs, sends them to a FastAPI service, and the model detects bugs, security risks, and style issues, posting feedback directly on the PR. A precision monitoring module tracks accuracy and disables AI reviews if performance drops.
AI startup idea validation platform using a multi-agent pipeline for market research, product definition, marketing strategy, analytics, and financial forecasting. It gathers signals from sources like Reddit and Hacker News to verify demand and outputs customer profiles, go-to-market plans, and 12-month financial projections.
Email: riyatrpthi@gmail.com
Location: Noida, India
LinkedIn: linkedin.com/in/riaatiwarii