Thanks for visiting my GitHub profile, it's great to meet you here! 😊
I'm a backend software engineer with over 10 years of experience, specializing in building scalable applications and innovative solutions using generative AI technologies. I'm passionate about creating intelligent systems that solve real-world problems.
Here are some quick things about me:
- 🌱 I'm currently learning various skills for developing applications with Large Language Models
- 📄 Here is my resume
- 📫 The best way to reach me is to send me an email
- ⚡ Fun fact: I once built an AI-powered chatbot that could write poetry in multiple languages! 🎭
# Skills that I'm good at experienced = { 'Programming Language': ['Python3', 'JavaScript', 'TypeScript', 'SQL'], 'LLM': ['GPT-4', 'Claude 3.5 Sonnet'], 'AI/ML Technologies': [ 'Retrieval-Augmented Generation (RAG)', 'Vector Databases', 'Semantic Search', 'ReAct Agent', 'Prompt Engineering' ], 'Libraries/Frameworks': ['LangChain', 'FastAPI', 'Node.js', 'Express.js', 'NumPy', 'Pandas'], 'Databases': ['PostgreSQL', 'Amazon RedShift', 'Amazon DynamoDB'], 'Tools': ['Docker', 'AWS', 'Git', 'Apache Kafka', 'Linux', 'Postman'], 'Software Engineering Knowledge': [ 'Data Structure & Algorithm', 'Microservices', 'System Design', 'API Design', 'Database Design', 'GraphQL' ] } # TODO: Skills that I want to acquire wishlist = { 'Programming Language': ['Go', 'Rust'], 'Advanced AI/ML Technologies': [ 'Fine-tuning LLMs', 'Parameter-Efficient Fine-Tuning (PEFT)', 'Low-Rank Adaptation (LoRA)', 'Quantized Low-Rank Adaptation (QLoRA)', 'Quantization', 'Multi-modal AI', 'Model Context Protocol (MCP)', 'Reinforcement Learning', 'Pre-Train BERT', 'Vector embeddings', 'Named Entity Recognition (NER) model', 'Automatic Speech Recognition (ASR) model' ], 'Libraries/Frameworks': ['PyTorch/TensorFlow', 'LangGraph', 'LlamaIndex'], 'Databases': ['Redis', 'MySQL', 'Neo4j'], 'Tools': ['CI/CD Pipeline', 'Kubernetes', 'Terraform', 'Ansible'] }- 🤖 Presto Voice AI - Developed an industry-leading AI voice assistant for drive-thru operations that:
- Implements real-time order taking with high accuracy using LLM and ASR technologies
- Features intelligent upselling capabilities that make 4x more context-specific upsell attempts
- Supports multiple deployment modes: Supervised AI, Pure AI, Agent-led, and Unsupervised AI
- Integrates seamlessly with existing drive-thru hardware and POS platforms
- Implements Menu Unification for centralized menu management across multiple restaurant brands
- Reduces staff workload by handling complex order scenarios autonomously
- 🏆 HackerRank
- ⚡ LeetCode
