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AI Engineer with 1+ years in Large Language Models & RAG Pipelines
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AI/LLM Engineer specializing in Large Language Models, RAG pipelines, Agentic AI, and end-to-end AI application development. Proficient in Python, LangChain, ChromaDB, TensorFlow, and PyTorch. Experienced in building production-grade LLM applications, semantic search systems, and intelligent automation workflows. Completed B.Tech in Computer Engineering with a CGPA of 8.45/10.
G H Patel College of Engineering & Technology (GCET), CVMU
B.Tech · Computer Engineering
N/A – June 30, 2026
Harptec Solutions Pvt. Ltd.
AI / LLM Engineering Intern
December 1, 2025 – Present
India
Ekaantik Software Solution
AI / ML Engineering Intern
May 1, 2025 – July 1, 2025
India
Self-Employed
Freelance AI Engineer
January 1, 2024 – Present
India
MLBB Insights Dashboard - AI-Powered Game Review Analysis
June 29, 2026 – Present
Designed and built an interactive dashboard analyzing 50,000+ Mobile Legends Bang Bang (MLBB) user reviews, enabling business and product insights from large-scale unstructured data. Implemented semantic search using ChromaDB and a Retrieval-Augmented Generation (RAG) pipeline with Groq LLMs for context-aware natural language Q&A over the review corpus. Optimized a batch-based embedding pipeline for efficient large-dataset processing on resource-constrained hardware, significantly reducing ingestion time.
View ProjectCold Mail Generator & Research Tool - LLM-Powered Outreach Automation
June 29, 2026 – Present
Built an end-to-end LLM application that scrapes job listings from any careers page URL, extracts structured role data (skills, experience, responsibilities), and auto-generates personalized cold outreach emails. Implemented skill-based portfolio matching using ChromaDB vector store — retrieves the most relevant portfolio projects based on job requirements and injects them into the generated email. Designed a multi-component LangChain pipeline (web loader, LLM chain, vector retriever) demonstrating practical agentic AI design patterns for real-world automation.
View ProjectEnsemble Learning for Crop Disease Detection
June 29, 2026 – Present
Hybrid ensemble of 5 deep learning models (ResNet50, EfficientNetB0, DenseNet121, MobileNetV2, Custom CNN) achieving 93% accuracy across 38 disease classes; includes Grad-CAM explainability and multi-scale input strategy.
Chess with AI
June 29, 2026 – Present
Chess engine with an AI opponent built in Python.
AI Mocker
June 29, 2026 – Present
AI-powered mock interview tool built with JavaScript and LLM APIs.
Dishly
June 29, 2026 – Present
AI-assisted recipe and food discovery application built in Python.
Introduction to TensorFlow for AI, Machine Learning, and Deep Learning
Coursera
January 1, 2025 – Present
Neural Networks and Deep Learning
DeepLearning.AI
January 1, 2025 – Present
Cloud Computing
NPTEL
January 1, 2024 – Present
Core Java Specialization
Coursera
January 1, 2024 – Present
Introduction to DevOps
Coursera
January 1, 2024 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, including game review analysis, outreach automation, and crop disease detection, shows a broad interest in applying AI across different domains. Their freelance work and internships indicate adaptability and a proactive approach to gaining experience. The focus on practical, real-world applications aligns well with an engineering culture that values tangible outcomes. The candidate's engagement with competitive programming also suggests a drive for continuous learning and problem-solving, which is a positive cultural indicator.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and self-direction through freelance work and numerous personal projects. Their ability to manage multiple internships and freelance projects concurrently with academic studies suggests good time management and dedication. The focus on building practical, end-to-end solutions indicates a results-oriented approach. However, without direct interview data, specific soft skills like teamwork, leadership, or conflict resolution cannot be fully assessed.