Building intelligent applications with GenAI, LangGraph, and Python | Actively seeking internships and full-time roles
- ๐ 4th-Year B.Tech in Computer Science and Engineering (AI & ML) at Lovely Professional University (CGPA: 8.42)
- ๐ผ Actively seeking internships (with PPO) or entry-level Software Engineering / AI roles.
- ๐ป Passionate about building Autonomous Agents, RAG pipelines, and Full-Stack Web Applications.
- ๐ Solved 100+ DSA problems and achieved HackerRankโs Software Engineer certification.
- ๐ง Currently exploring Generative AI, Multi-Agent Systems, MCP, and Next.js.
- ๐ Check out my portfolio: Portfolio
An autonomous multi-agent system powered by a LangGraph pipeline, Tavily web search, and Google Gemini LLMs. It researches public companies in real-time and delivers a structured INVEST / PASS / MONITOR verdict with confidence scoring and factor analysis using Server-Sent Events (SSE).
โฆ What makes it special:
- ๐ค Multi-Agent Pipeline: Orchestrates complex research tasks using LangGraph.
- ๐ Live Web Search: Integrates Tavily for real-time data fetching.
- ๐ง Advanced Reasoning: Uses Google Gemini LLMs to output confidence scores and factors.
- โก Real-Time Streaming: Streams verdicts to the frontend instantly via SSE.
A Django-based expense-sharing system that automatically works out the minimum number of payments needed to settle group debts using a custom netting algorithm.
โฆ What makes it special:
- ๐งฎ Custom Netting Algorithm: Computes the absolute minimum transactions to settle debts.
- ๐ฅ Group Management: Secure login, friend management, and group creation.
- ๐ Flexible Splitting: Handles exact amounts, percentages, or equal shares seamlessly.
- ๐ Real-Time Updates: Live balance calculation and optimized Django ORM queries.
An autonomous planning agent in Python that follows an Observe โ Reason โ Plan โ Act โ Reflect loop to prioritize tasks, catch overload, and reschedule automatically.
โฆ What makes it special:
- ๐ Autonomous Loop: Implements full Observe โ Reason โ Plan โ Act โ Reflect cycles.
- ๐ฏ Rule-Based Scoring: Evaluates task priority and effort without heavy ML overhead.
- ๐ง Memory System: Learns from missed tasks to improve future scheduling estimations.
- โฑ๏ธ Overload Prevention: Automatically reschedules tasks to prevent schedule burnout.
An ML-powered prototype that automates candidate screening by converting resumes and job descriptions into vectors to calculate similarity scores. Reached 88% match accuracy and cuts initial screening time by 37%.
โฆ What makes it special:
- ๐ Vector Similarity: Converts unstructured resumes and JDs into comparable embeddings.
- โก Efficiency Boost: Demonstrably reduces HR screening time by 37%.
- โ๏ธ Fairness Focused: Built with considerations for bias mitigation (gender/experience proxies).
- ๐ฏ High Accuracy: Achieved an 88% match accuracy on candidate evaluation.
- GenAI Ecosystem: Multi-Agent Systems, LangChain, LangGraph, MCP, Advanced RAG
- Deep Learning: NLP, TensorFlow, PyTorch
- Web & Core CS: Next.js, React, DSA
๐ง Email: me.yuvraj432@gmail.com
๐ Portfolio: yuvraj-portfolio-2026.vercel.app
๐จโ๐ผ LinkedIn: linkedin.com/in/yuvraj432
"Code. Learn. Build. Share. Repeat."