name : Sujal Maheshwari
degree : B.Tech CSE (AI & Data Science) Β· Graphic Era University, Dehradun
location : Aligarh, Uttar Pradesh, India
status : Final Year Β· 2026
seeking : Internships Β· Research Roles Β· Freelance (AI / LLMs / Full-Stack)
exploring: Agentic AI workflows with MCPI build production-grade LLM systems, train language models from scratch, and ship agentic infrastructure that solves real engineering problems. My work spans the full vertical β custom tokenizers and model architectures, RAG pipelines, LangGraph agent loops, MCP servers, FastAPI backends, and everything in between.
| Metric | Result | Context |
|---|---|---|
| π Latency reduction | 96% | 7 min β 30 sec Β· LangGraph parallel agents |
| π― RAG accuracy | 62% β 86% | Repo indexing + AST chunking pipeline |
| π€ LLM parameters | 125M | Trained entirely from scratch Β· Librarian series |
| π Records processed | 130K+ | Shopify pipeline Β· 5 hrs β under 45 min |
| π¨βπ« Students mentored | 130+ | Workshops & project guidance Β· Graphic Geeks Club |
Β π΅Β AI Architect Β· STARTHACK.IO (Freelance) Β |Β Sept 2025 β Feb 2026 Β· Dehradun, Hybrid
- Built parallel LangGraph agents for automated code evaluation β cut end-to-end latency from 7 min to 30 seconds
- Architected a real-time voice-to-voice technical interview system using GPT Real-Time APIs with dynamic persona switching and low-latency audio streaming
- Designed scalable repository indexing and code chunking strategies for a RAG pipeline, improving retrieval accuracy from ~62% to ~86%
LangGraph GPT-4o GPT Real-Time API RAG AST Azure GitHub Actions
Β π£Β AI Data Scientist Intern Β· Basal AI (Remote) Β |Β May β Aug 2025 Β· Bengaluru
- Fine-tuned GPT-4.1 on domain data via Azure AI Foundry β content generation turnaround from 3 days β 15 minutes at 96% baseline accuracy
- Built a parallelised Shopify scraper β reduced product and variant compilation time from 5+ hours to under 45 minutes across 130K+ records
- Built Teams-integrated agents using Power Automate and Copilot Studio for internal analytics workflows and automated reporting
- Configured CI/CD pipelines with GitHub Actions for automated testing and deployment of AI inference services
GPT-4.1 Azure AI Foundry Python Power Automate Copilot Studio GitHub Actions
Β π’Β LLM Engineer Β· Patent Work Β |Β Apr β Jul 2024 Β Β·Β App No: 202411035697
- Built an NLP keyword-extraction chatbot using rule-based and statistical filtering on hospitality-domain data
- Fine-tuned GPT-2 on proprietary corpora and implemented a RAG pipeline for document-grounded responses
- Contributed to a patentable AI system β Namaste-Enabled Service Robot for Hospitality Industry
NLP GPT-2 RAG LLM Hospitality AI
π§ DriftGuardAgentic Infra Β· Semantic Memory Layer Mistake-memory system for AI agents. Stores causal chains ( Dual interface: MCP server for tool-based agents + in-process guard API for LangGraph loops. Full pytest coverage, CI via GitHub Actions. |
π Bot StreetSimulation Β· HFT Environment for LLM Agents Full algorithmic trading simulator on Apache Kafka (KRaft). Price-time priority order book, circuit breakers, sentiment engine, 15+ quant indicators (RSI, MACD, Bollinger, OFI, VaR, CVaR). An MCP server lets LLM agents trade live against bots. Participants: Market Maker Β· Momentum Β· Mean Reversion Β· Random Β· LLM Agent |
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π Librarian SeriesPersonal Research Β· LLM From Scratch 125M parameter causal LM built end-to-end: custom 16K BPE tokenizer, GPT architecture with RoPE + RMSNorm + SwiGLU MLP, trained on WikiText-103 + TinyStories. 6.19 perplexity on validation. LoRA fine-tuned on DailyDialog. Ships with a full config-driven SFT framework.
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π§ ToolStoreLLM Infra Β· Automatic MCP Server Builder Plain-English tool description β semantic search + cross-encoder reranking β clone matched repos β static AST security scan β single runnable MCP server. 100% build accuracy and AST validity across 32,767 evaluated subsets. β‘ PeakPulseAI Product Β· Customer Support Intelligence 3-node LangGraph pipeline β classify β route β resolve β with deterministic escalation and rule-based fallback at <5ms, $0 cost. 50 concurrent queries via |
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π° NewsCheckPipelines Β· Audio β Structured Summary YouTube link β |
π₯ MediMatchAI/ML Β· Medical Document Parsing Organ transplant prioritization using LLaMA 3.2. Parses medical documents and ranks patients by criticality score using structured LLM output. Node.js/Express backend. |
| Certification | Issuer |
|---|---|
| π· DP-900 Β· Microsoft Azure Data Fundamentals | Microsoft |
| π Generative AI Specialization | AWS + DeepLearning.AI |
| π΅ Data Analytics | IBM |
| π‘ AWS Certified Cloud Practitioner | Amazon Web Services |
| π | Top Performer Β· Smart India Hackathon 2024 β AI for Smart Education |
| π | Patent Contributor Β· Namaste-Enabled Service Robot for Hospitality Industry Β· App No: 202411035697 |
| π | Founder & Technical Team Lead Β· Graphic Geeks Club β mentored 130+ students via workshops and project guidance |
| π€ | Published on HuggingFace β 125M param LLM trained from scratch, publicly available |
| π | Currently building agentic AI workflows with MCP |



