AI/ML Engineer with 2.5+ years of experience designing and deploying production-grade AI systems. Skilled in Machine Learning, Deep Learning, Generative AI, LLMs, RAG, AI Agents, and workflow automation. Experienced with Python, FastAPI, Flask, LangChain, LangGraph, Langflow, LlamaIndex, CrewAI, Model Context Protocol (MCP), transformer models, vector databases, graph databases, and knowledge graphs. Proficient in building scalable backend APIs, AI pipelines, and automation workflows using n8n and Zapier, with hands-on experience deploying applications using Docker and CI/CD.
Building intelligent systems that solve real-world problems
🎓 B.Tech in Computer Science & Engineering
Swami Vivekanand Institute of Engineering & Technology • CGPA: 8.5/10
📍 Banur, Punjab, India
Building AI solutions at scale for leading organizations
Developed and maintained PHP-based web applications with an emphasis on backend performance, clean architecture, and reliable API integrations. Worked on responsive UI integration and optimized server-side logic to improve application efficiency and user experience.
Production-grade AI solutions across multiple domains
Built an end-to-end deep learning pipeline for classifying product images as Mobile Phones or Laptops using TensorFlow and MobileNetV2. Implements complete ML workflow including dataset preparation, training, evaluation, and deployment-ready inference via FastAPI. Features binary classification with transfer learning, achieving high accuracy on custom datasets for e-commerce automation.
Developed a machine learning system to detect deepfake/synthetic audio using Wav2Vec2 embeddings and classical ML classifiers. Achieved 92.86% accuracy with Logistic Regression on the Real vs Fake Human Voice dataset (700k samples). Pipeline extracts 768-dimensional feature vectors, handles variable-length audio, and implements preprocessing with StandardScaler normalization. Trained and compared Logistic Regression (best), SVM, and Random Forest models.
Architected an enterprise-grade multi-agent RAG system using LangGraph orchestration. Implements specialized agents for document retrieval, synthesis, fact-checking, and response generation. Features dynamic routing, agent collaboration, and context-aware memory management with 95%+ answer accuracy on domain-specific queries.
Fine-tuned BERT and T5 transformer models for domain-specific sentiment analysis. Implemented transfer learning, data augmentation, and advanced preprocessing. Achieved 94% F1-score on custom dataset with balanced precision-recall. Deployed with FastAPI for real-time inference.
Fine-tuned LLaMA 3.1 8B for classifying Linux commands and natural language into predefined intents. Built for AI terminal assistants and DevOps automation. Achieved 96% accuracy using LoRA fine-tuning with custom prompt-completion dataset.
Built a production-grade podcast processing system with speaker diarization, multi-host/guest identification, and automatic music filtering. Implemented real-time line-level editing, WebSocket-based progress tracking, and chunked long-form processing using a sliding-window approach for LLM limits. Integrated LLM-driven summarization, sentiment analysis with timestamps, and semantic search via Pinecone, with transcripts securely stored in AWS S3.
Built an intelligent complaint handling system using CrewAI multi-agent framework. Automatically processes text and audio inputs, classifies issues, verifies against policies, generates responses, and integrates with CRM. Reduced response time by 70% while maintaining quality.
Engineered a sophisticated n8n workflow with dual AI agents, persistent MongoDB memory, and intelligent routing. Implements context-aware conversations, webhook triggers, and modular architecture for scalable automation across multiple domains and use cases.
Developed a remote control system for Hisense TV and Fire TV using ADB and MQTT protocols. Enables seamless device communication, Android automation, and real-time command execution for smart home integration.
Built an ML-powered web scraping system to handle dynamic popups, CAPTCHA detection, and anti-bot measures. Trained a CNN-based popup detection model enabling automated interaction and seamless scraping across 50+ websites, achieving 98% accuracy using Selenium with headless Chrome.
Built a high-performance speech-to-speech system with industry-leading latency. Uses Deepgram for STT, Groq LLM for rapid inference, and ElevenLabs for natural TTS. Implements streaming responses, ChromaDB for knowledge retrieval, and optimized pipeline achieving consistent sub-2-second response times.
Developed a production-ready chatbot with LangChain and ChromaDB. Features voice interaction, real-time streaming via WebSocket, conversation memory, and automatic HTML transcript generation sent via SMTP. Handles context across sessions with personalized responses.
Built a production-grade video conferencing app with FastAPI and WebRTC. Features instant meeting creation, multi-participant support, text chat, user authentication, OTP recovery, and optional recordings. Optimized for low latency and high concurrent user capacity.
Comprehensive skill set spanning AI/ML, automation, scraping, and backend development
Open to exciting opportunities and collaborations
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