π Full-Stack & AI/ML Engineer building scalable applications, intelligent systems, and cloud-native architectures. π§ Deep focus on Generative AI, LLMs, RAG pipelines, and AWS AI services. π‘ Constantly exploring cutting-edge tech and pushing the limits of automation & intelligence.
- π€ AI/GenAI: LLMs, RAG, Vector Embeddings, Transformers, LangChain, AWS Bedrock
- π Full-Stack Web Development (MERN & MEAN)
- β‘ AI/ML & Deep Learning (PyTorch, TensorFlow, Keras, NumPy, SciPy)
- βοΈ Cloud & DevOps: AWS, Docker, CI/CD Pipelines, Infrastructure Automation
- π Backend APIs with Node.js, Express.js, FastAPI
- π Databases: MongoDB, PostgreSQL, DynamoDB
- π¨ Frontend Frameworks: React.js, Next.js, Angular, TypeScript
- β‘ Hacker Mindset: Problem solver, fast learner, system builder
π‘ Skilled in designing cloud-native, scalable, and secure architectures β from provisioning EC2 instances & VPCs, managing IAM roles/policies, serving static content via S3 + CloudFront, to deploying containerized microservices on ECS and leveraging AWS Bedrock & Polly for GenAI-powered applications.
- π Building RAG-based intelligent systems using vector databases & embeddings
- π£ Voice/AI pipelines using AWS Polly + Bedrock
- 𧬠Exploring transformer architectures & attention mechanisms from first principles
- π Visualizing embedding spaces & token relationships for deeper model interpretability
- π Attention Is All You Need
- π‘ Tokenization Explained
- π§© Vector Embeddings
- π Vector Embeddings Visualizer
- π Retrieval-Augmented Generation (RAG)
β‘ "Code. Learn. Build. Hack. Deploy. Repeat." β‘


