name: Ishaan Rastogi role: AI/ML Engineer in the making location: India (IN) current_focus: - AI/ML product development - NLP and recommendation systems - LLMs for growth of Indian industries - Vertical & Horizontal Suites with AI Pipelines - Java DSA and core CS problem solving learning_now: - model deployment and optimization - production-friendly data workflows - flutter framework and dart language - DSA in Java philosophy: build fast, learn faster, ship practical solutions motto: Imagination should never die! 🪽
- Building projects that move from notebooks to usable products.
- Deepening engineering fundamentals with Java, DSA, and system-level thinking.
- Exploring AI ideas where product impact is visible, not just experimental.
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 Applied AI Products From idea to usable prototypes |
 Problem Solving Consistency in coding rounds |
 ML Intelligence Layers Recommendations and text systems |
 Model Ops Basics Making ML production ready |
| Repository | Domain | Why it matters |
|---|---|---|
| Med-Map | Health-tech | Practical web-first project direction with product thinking. |
| SwayUp.ai | Applied AI | Core AI build track for experimentation and iteration. |
| IsoTrust.ai | AI + Trust | Blends AI implementation with reliability/compliance angle. |
| Prime-Mood | Recommender/NLP | Recommendation-oriented concept with user-intent signals. |
| AIML_BigProjects | AI/ML Portfolio | Collection of larger experiments and model pipelines. |
| Java | DSA + OOP | Foundation repository for algorithmic and interview prep. |
💡 I've utilised this stack across my projects, but I may not be proficient with all of it. Always learning and exploring!
- Built and maintained multiple AI-first repositories in active development.
- Consistent project momentum across Python, Jupyter, Java, and JavaScript tracks.
- Expanded from learning experiments toward product-oriented implementations.
| Month | Current Build Track | Shipping Goal |
|---|---|---|
| Apr 2026 | AIML_BigProjects: model experimentation and iteration | Improve model quality with cleaner evaluation and iteration loops |
| PyJ-DSA: algorithm implementation consistency work | Improve problem-solving speed through structured practice | |
| Mar 2026 | LeetSync: integration and stability improvements | Close the month with production-ready demos and clearer docs |
| IsoTrust.ai: training pipeline stabilization | Stabilize model workflows for repeatable monthly progress | |
| Feb 2026 | AIML_MiniProjects: training pipeline stabilization | Convert experiments into reusable training components |
♻️ This block auto-updates at 1st day 6 AM every month!.
Open to building in AI/ML, NLP, recommender systems, and backend-heavy product teams.