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AI Engineer with less than a year in LLM Applications & RAG Pipelines
AI Engineer with hands-on experience building and deploying LLM-based applications, Retrieval-Augmented Generation (RAG) pipelines, and NLP solutions. Proficient in LangChain, Hugging Face Transformers, Python, and vector databases (FAISS, Pinecone). Demonstrated ability to architect end-to-end AI systems from data ingestion through model deployment, achieving 85-90%+ answer relevancy benchmarks. Adept at prompt engineering, agentic workflows, and optimizing system accuracy and scalability using Docker and CI/CD practices.
DAV University
B.Tech · Computer Science & Engineering
August 1, 2022 – June 30, 2026
S.D.S.V.M Talwara
Senior Secondary (12th Grade)
June 1, 2021 – May 31, 2022
S.D.S.V.M Talwara
Secondary (10th Grade)
June 1, 2018 – May 31, 2019
Coder Roots
Deep Learning & NLP Trainee
June 1, 2025 – July 31, 2025
India
07 Services
Data Science & Machine Learning Trainee
June 1, 2024 – September 30, 2024
India
PDF Q&A RAG Application
June 24, 2026 – Present
• Built an intelligent PDF Question-Answering system using LangChain, PyPDF, Hugging Face Transformers, and FAISS for document querying. • Developed complete pipeline: PDF text extraction, chunking, embedding creation using sentence-transformers, and similarity-based semantic retrieval. • Integrated open-source LLaMA models for accurate response generation; optimized retrieval via chunk-size tuning and metadata filtering. • Achieved 85-90% relevance accuracy on user test cases; deployed with a clean Streamlit-based UI on Hugging Face Spaces.
YouTube Q&A RAG Application
June 24, 2026 – Present
• Developed a YouTube-based Retrieval-Augmented Generation (RAG) application for transcript-based question answering using LangChain, Streamlit, and Hugging Face Transformers. • Implemented end-to-end pipeline: YouTube transcript extraction, text chunking, embedding generation via sentence-transformers, and semantic retrieval using FAISS vector database. • Integrated open-source LLMs for contextual answer generation; achieved 90%+ answer relevancy on evaluation queries. • Designed interactive Streamlit interface and deployed on Hugging Face Spaces for public access.
Indian Cybercrime Legal RAG Assistant
June 24, 2026 – Present
• Built a Retrieval-Augmented Generation (RAG) application for Indian cyber law research, enabling users to describe situations in plain English and receive AI-generated legal analysis grounded in the IT Act 2000, IPC, and BNS 2023. • Designed and curated a structured dataset of 153 real-world cybercrime scenarios across 12 categories (hacking, financial fraud, cyberstalking, data theft, etc.) with mapped legal sections, explanations, and punishments. • Engineered a semantic search pipeline using FAISS vector indexing and Sentence-Transformers (all-MiniLM-L6-v2) embeddings to retrieve the most relevant legal provisions for any user query with sub-second latency. • Implemented a LangChain-based RAG chain that retrieves context from the FAISS vector store, formats multi-field legal metadata, and generates grounded responses via Groq-hosted LLaMA-3.3-70B with temperature-controlled inference. • Developed a conversational Streamlit interface with chat history, configurable retrieval depth (top-k), example query shortcuts, source transparency (showing retrieved entries with full metadata), and sidebar API key management. • Ensured privacy-first architecture by running all embedding generation locally on-device, with no user data leaving the machine during indexing; only the final query is sent to the Groq LLM API for inference.
Cultural Fit Analysis
The candidate's portfolio of personal projects, particularly the Indian Cybercrime Legal RAG Assistant, shows a strong interest in applying AI to real-world, impactful problems. This indicates a proactive and problem-solving mindset. The breadth of skills across LLM frameworks, NLP, vector databases, and deployment tools suggests adaptability and a willingness to learn new technologies. The focus on open-source tools and models also aligns with a collaborative and community-driven culture. However, the candidate's experience level is very junior (still pursuing a B.Tech degree), which might require more mentorship and structured guidance in a senior team environment.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and a project-oriented approach, which aligns well with roles requiring self-starters. The focus on end-to-end application development, including UI and deployment, suggests a practical, results-oriented mindset. The privacy-first architecture in the legal RAG project indicates an awareness of ethical considerations and robust design. However, without direct interview data, assessing collaboration, stress handling, and communication clarity in a team setting is not possible.