AI/ML Engineer
I design and ship production-grade AI systems — from neural networks and RAG pipelines to full-stack, real-time products.
The journey of a Computer Engineer turned AI Engineer
I'm a Computer Engineer and AI Engineer passionate about building intelligent systems that solve real-world problems. My journey started with computer engineering and evolved into a deep fascination with artificial intelligence.
I specialize in transforming complex AI research into production-ready applications — from ML models and Computer Vision pipelines to LLM-powered chatbots and RAG systems.
To work at the intersection of AI research and engineering, building cutting-edge AI systems that are innovative, scalable, and impactful. I aim to contribute through open source, research, and mentorship.
Started with C and Python, fell in love with building things that think.
Began B.E. — algorithms, systems, and the foundations of computing.
Dived deep into ML, DL and NLP; shipped my first end-to-end models.
Mastered LangChain, LangGraph and RAG, building production AI systems.
Designing multi-agent systems and scalable AI products end-to-end.
Technologies and tools I work with
Production-ready AI systems I've built
A multi-agent AI travel planner that generates personalized trips with real-time flight and hotel recommendations.
Retrieval-augmented generation over 50k+ documents with citations and source tracing.
Real-time helmet violation, triple riding, and license plate detection with automated e-Challan generation.
A modern, responsive e-commerce frontend with a clean shopping experience.
A full-stack restaurant website with online reservations and menu management.
Education, work, and research milestones
Tribhuvan University
Specialized in intelligent systems. Coursework in algorithms, OS, networks, DBMS, and machine learning. Graduation project: a production RAG system.
Personal Project
Designed a graph-orchestrated multi-agent system with checkpointing and human-in-the-loop control, open-sourced on GitHub.
DeepLearning.AI
Completed Andrew Ng's specialization covering neural nets, CNNs, sequence models, and transformers.
Exploring the frontiers of artificial intelligence
Classical & deep models — regression, gradient boosting, neural architectures and rigorous evaluation.
Detection, segmentation and vision-language models optimized for real-time edge inference.
Text classification, embeddings and semantic search with transformer-based encoders.
Fine-tuning, quantization, inference optimization and responsible deployment of LLMs.
Hybrid retrieval, reranking, citations and grounded generation for trustworthy answers.
Composable LLM applications with chains, memory, tools and structured output.
Stateful, cyclic agent graphs with checkpoints and human-in-the-loop control.
Autonomous, tool-using agents that plan, act and collaborate to solve complex tasks.
Milestones and recognition
DeepLearning.AI
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