B.Tech Student | 4th Year AI & Data Science | Aspiring Applied AI Engineer
Building intelligent systems that automate workflows, power decisions, and solve real-world problems with AI.
I'm passionate about Agentic AI Systems and AI-powered Automation. Rather than building models that live in notebooks, I focus on creating deployable systems that actually replace manual workflows. My journey combines strong software engineering fundamentals with deep AI/ML expertise to build intelligent, production-ready solutions.
Philosophy: AI + Automation + Practical Software = Real Impact
- π€ Design and build agentic AI systems with intelligent decision-making
- π Create AI-powered automation tools that replace manual workflows
- π§ Develop LLM-based applications and intelligent assistants
- π Build recommendation systems and decision support tools
- ποΈ Design system architectures optimized for AI workloads
- π» Full-stack development with clean, scalable code
- Java β Primary language, strong fundamentals
- Python β ML, automation, AI workflows
- TypeScript β Type-safe frontend & backend
- JavaScript β Dynamic, rapid prototyping
- Machine Learning fundamentals
- AI-powered automation systems
- LLM-based tools & chatbot integration
- Recommendation systems (GNN-based exploration)
- AI workflow system design
- React + Vite
- TypeScript
- Tailwind CSS
- REST API integration
- Frontend system design
- Git & GitHub (version control, collaboration)
- Vercel, GitHub Pages (deployment)
- API integration (Gemini API, YouTube API)
- System design for AI workflows
- Docker basics
- SQL
- PostgreSQL
- NoSQL
- Data modeling for AI systems
An enterprise-grade Agentic AI platform that transforms raw CSV/Excel datasets into actionable business intelligence using natural language. Users simply upload data and ask business questions, while a network of specialized AI agents collaborates to analyze data, generate machine learning pipelines, execute code securely, self-heal execution errors, and deliver explainable insights with interactive visualizations.
Why it matters: Demonstrates advanced AI Engineering through multi-agent orchestration, automated machine learning, self-healing code execution, model persistence, and enterprise-ready AI workflows.
Tech Stack: React, FastAPI, Python, LangChain, Pandas, NumPy, Scikit-learn, XGBoost, PyTorch, TailwindCSS, Server-Sent Events (SSE), MCP, OpenRouter, Gemini, Groq, OpenAI, Anthropic
Key Highlights:
- π§ 20 specialized AI agents collaborating across the ML pipeline
- β‘ 70+ modular AI skills loaded dynamically based on task requirements
- π€ Natural language β Automated Machine Learning workflow
- π Self-healing execution engine with intelligent error recovery
- π Automated visualizations, KPI generation, and business insights
- 𧩠Multi-provider LLM routing with automatic fallback mechanisms
- π MCP integrations including GitHub, Google Drive, Kaggle, Hugging Face, PostgreSQL, Browser Automation, and more
- πΎ Persistent ML models for future inference and predictions
Focus Areas: Agentic AI β’ Multi-Agent Systems β’ AutoML β’ LLM Orchestration β’ Machine Learning β’ AI Engineering β’ Full Stack AI
An intelligent agent system that autonomously handles operational workflows using AI-driven decision-making. The system plans tasks, interacts with tools and APIs, executes multi-step processes, and includes reliability and robust error handling.
Why it matters: Demonstrates practical agentic AI architecture for autonomous workflow automation and operational intelligence.
Tech Stack: Python, LLMs, API Integration, Task Orchestration
Focus Areas: Agentic AI β’ Workflow Automation β’ AI Operations
An intelligent machine learning system that recommends optimal crops based on soil parameters and environmental conditions, helping farmers make data-driven agricultural decisions.
Why it matters: Demonstrates the practical application of machine learning for sustainable agriculture.
Tech Stack: Python, Machine Learning, Data Analysis
Focus Areas: Applied ML β’ Decision Support Systems β’ Agricultural AI
An AI-powered utility system for intelligent file management and system resource optimization. Supports automated file operations, intelligent organization, and seamless handling of local and external storage.
Why it matters: Demonstrates AI at the OS level for practical system automation.
Tech Stack: Python, AI automation, system APIs
Focus Areas: AI-powered System Tools β’ OS-level Automation
- π Advanced Data Structures & Algorithms (placements + problem-solving)
- ποΏ½οΏ½οΏ½ System design for AI applications at scale
- π€ Multi-agent AI workflows and coordination
- π‘οΈ Failure handling, reliability, and scaling in AI systems
- π Production-grade AI systems architecture
- π Portfolio: rohitsurya.me
- πΌ LinkedIn: linkedin.com/in/rohit-surya-385143290
- π GitHub: @RohitSurya2809
- βοΈ Email: sankarirohitsurya@email.com
I like building AI tools that actually do things, not just models in notebooks.
I'm obsessed with:
- β¨ Turning ideas into deployable systems
- π Mixing AI + automation + practical software
- π― Real-world impact over academic exercises
- π Building the future of intelligent applications
I'm excited about:
- π¬ AI & automation projects
- π οΈ Open-source contributions
- π¬ Technical discussions on agentic AI
- π± Mentoring and learning from the community
- π Building things that matter
Feel free to reach out if you'd like to collaborate, discuss AI systems, or just chat about tech!
If you find my work interesting or useful, please consider:
- β Starring my repositories
- π Connecting on LinkedIn
- π¬ Sharing feedback and ideas
Let's build intelligent systems together! π
Last updated: July 2026