What I'm Doing Now
Inspired by Derek Sivers' /now page movement. A public snapshot of what I'm currently building, learning, reading, and working toward.
Currently Building
CareerAI – AI Career Coach
DetailsBuilding an AI-powered career assistant that helps users improve resumes, prepare for interviews, generate cover letters, and receive personalized career guidance.
GMSAC – Gujarati Sentiment Analysis Corpus
DetailsMaintaining and documenting a benchmark Gujarati sentiment analysis dataset while evaluating transformer-based language models for low-resource languages.
AI Portfolio & Technical Blog
Building a documentation-style portfolio featuring AI engineering articles, technical case studies, and project documentation focused on LLMs, RAG, AI Agents, Computer Vision, and MLOps.
Currently Learning
Large Language Models & AI Systems
Studying how modern LLM-powered applications are designed, optimized, and deployed in production environments.
- Transformers
- Context Windows
- Prompt Engineering
- RAG
- Vector Databases
- AI System Design
AI Agents
Learning how autonomous AI systems perform planning, memory management, tool usage, and workflow orchestration.
- LangGraph
- MCP
- Tool Calling
- Agent Memory
- Multi-Agent Workflows
MLOps & Production AI
Understanding deployment pipelines, monitoring, model versioning, Docker, FastAPI, and scalable AI infrastructure.
- FastAPI
- Docker
- CI/CD
- Model Serving
- Monitoring
Currently Reading
Designing Machine Learning Systems
By Chip Huyen
Learning practical approaches to building reliable, scalable, and production-ready machine learning systems.
Designing Data-Intensive Applications
By Martin Kleppmann
Understanding distributed systems, scalable backend architecture, reliability, and data engineering fundamentals.
Current Reading Topics
Actively reading research papers on: Large Language Models, Retrieval-Augmented Generation (RAG), AI Agents, Computer Vision, Healthcare AI.
Current Goals
Actively preparing for and applying to AI Engineer, Machine Learning Engineer, and Generative AI roles while strengthening practical engineering skills.
Building a documentation-style technical blog covering LLMs, RAG, AI Agents, Computer Vision, Machine Learning, AI Systems, and MLOps.
Creating end-to-end AI applications that demonstrate practical engineering, deployment, and real-world problem solving.