AI Engineer | Building Production-Grade AI Systems | IIIT Naya Raipur (DSAI '28)

I'm a B.Tech student in Data Science and Artificial Intelligence at IIIT Naya Raipur, specializing in production-grade AI systems, agentic architectures, and full-stack engineering. My work focuses on building AI systems that go beyond proof-of-concepts to real-world deployment.

I started coding in Class 9 during COVID and have since evolved from basic applications to secure, scalable AI systems with strong engineering foundations. My approach combines deep AI research understanding with rigorous software engineering principles — security, system design, and scalability.

Large Language Models & Fine-Tuning

• Fine-tuned Llama 3.1 8B using LoRA on domain-specific datasets (10,000+ examples)
• Achieved production-level performance (final loss: 0.157) with quantization and optimization
• Experience with prompt engineering, few-shot learning, and context optimization
• Working knowledge of transformer architectures, attention mechanisms, and positional encodings

Advanced RAG & Retrieval Systems

• Designed hybrid retrieval pipelines combining dense (FAISS) and sparse (BM25) retrieval
• Implemented re-ranking with MonoT5 and query expansion using HyDE (Hypothetical Document Embeddings)
• Built semantic chunking strategies with overlap and context preservation
• Achieved 92% precision in production retrieval systems

Multi-Agent Orchestration

• Architected 20+ specialized AI agents executing in parallel with <500ms latency
• Designed context-aware agent routing with task decomposition and subtask delegation
• Implemented agent communication protocols with shared memory and state management
• Built semantic code analysis agents for refactoring, architecture review, and documentation

Knowledge Graphs & Reasoning

• Integrated Neo4j Knowledge Graphs with LLM reasoning for explainable AI
• Designed entity extraction pipelines with relationship mapping and graph traversal
• Built clause-level explainability systems for complex decision-making

Robotics & Computer Vision

• Real-time perception using YOLO, Deep SORT for object detection and tracking
• ROS2-based autonomous systems with MoveIt for motion planning
• Gazebo simulation environments for testing AI-based navigation
• Research experience at IIT Jodhpur on robotics perception pipelines

Security & Safety

• Implemented input validation, output filtering, and prompt injection prevention
• Built rate limiting, authentication layers for LLM-based APIs
• Designed audit trails and logging for AI decision transparency

Performance Optimization

• Model quantization (4-bit, 8-bit) for edge deployment
• Caching strategies for embeddings and LLM responses
• Parallel execution of agent workflows with async processing
• Token optimization to reduce latency and costs

Production Deployment

• Containerized AI systems using Docker with orchestration
• Built FastAPI microservices with proper error handling and monitoring
• Integrated AI systems with blockchain for tamper-proof audit logs
• Designed scalable architectures handling concurrent requests

I'm currently strengthening my CS foundations to build more robust AI systems:

Data Structures & Algorithms

• Deep understanding of trees, graphs, heaps, hash tables, and tries
• Algorithm design: dynamic programming, greedy algorithms, backtracking
• Complexity analysis and optimization techniques
• Active competitive programming practice

System Design & Architecture

• Designing scalable microservices with load balancing and caching
• Understanding database normalization, indexing, and query optimization
• Building distributed systems with message queues and event-driven architectures
• API design patterns and RESTful principles

Operating Systems & Networking

• Process management, threading, and synchronization
• Memory management and virtual memory concepts
• Network protocols (TCP/IP, HTTP/HTTPS, WebSockets)
• Understanding of containerization and virtualization

Database Systems

• Relational databases: PostgreSQL, MySQL with advanced querying
• NoSQL: MongoDB for document storage and flexible schemas
• Graph databases: Neo4j for relationship-heavy data
• Transaction management, ACID properties, and concurrency control

This foundation ensures my AI systems are built on solid engineering principles rather than just stitching libraries together.

LLMs: Llama, GPT-4, Claude, Gemini | Fine-tuning: LoRA, QLoRA
RAG: FAISS, ChromaDB, BM25, MonoT5 | Frameworks: LangChain, LlamaIndex
ML: TensorFlow, PyTorch, Scikit-Learn | CV: YOLO, OpenCV, Deep SORT
Robotics: ROS2, Gazebo, MoveIt | Graphs: Neo4j, NetworkX

Languages: Python, C++, JavaScript, TypeScript, C
Backend: Node.js, FastAPI, Express.js | Frontend: React, Next.js, Tailwind CSS
Databases: MongoDB, PostgreSQL, Neo4j | Auth: Auth0, JWT, OAuth
DevOps: Docker, Git, Linux | APIs: REST, GraphQL, WebSockets

Microservices, Event-Driven Architecture, Caching (Redis)
Message Queues, Load Balancing, Database Optimization
Distributed Systems, Blockchain Integration

• 🥇 AI Engineer Intern — Atlan (Working on production AI systems)
• 🥇 1st Place — Hack-a-Sol (AI Financial Intelligence Platform)
• 🥈 2nd Place — Hack-o-Harbor (E-Cell, IIIT Naya Raipur)
• 🎓 Research Intern — IIT Jodhpur (Robotics & AI Perception)
• 👨‍💻 Dev Club Head — Leading technical community & mentoring
• 🧠 Built multiple production-grade AI systems deployed in real environments

Depth Over Breadth: I don't just use AI libraries—I understand the math, architectures, and engineering behind them. I've fine-tuned models, built custom retrieval systems, and designed multi-agent orchestration from scratch.

Engineering Rigor: Every AI system I build has proper error handling, security layers, monitoring, and is designed for production. I understand that 80% of AI engineering is traditional software engineering done right.

Research to Production: I bridge the gap between academic papers and deployed systems. I read research, implement it, optimize it, and ship it with proper engineering practices.

Systems Thinking: I approach AI problems with a systems mindset—considering latency, cost, security, scalability, and maintainability from day one, not as afterthoughts.

Short-term: Deepening CS fundamentals while building increasingly sophisticated AI systems. Contributing to open-source AI tooling and working on agentic AI architectures.

Long-term: Becoming a Senior AI Engineer / AI Architect who can design and lead production AI systems at scale. Building expertise in AI safety, alignment, and robust system design.