AI Developer Nepal
End-to-end AI and machine learning development — from data preprocessing and model training to REST API deployment and production monitoring. I build ML solutions that solve real business problems, not just notebook experiments.
What I build
Artificial intelligence is no longer reserved for large technology companies. With Python, TensorFlow, scikit-learn, and the OpenAI API, businesses of all sizes can embed intelligent features into their products — automated classification, prediction, generation, and analysis — at a cost that makes commercial sense.
As an AI developer in Nepal, I bring together machine learning expertise and software engineering discipline. A model that achieves 94% accuracy in a Jupyter notebook is worthless if it cannot be integrated into your product reliably. I handle both sides: the data science and the production engineering.
My AI work spans NLP (including the Fake News Detection System which achieved 94% classification accuracy), computer vision, predictive analytics, and LLM integrations using the OpenAI API and open-source models.
NLP & Text
BERT, LSTM, TF-IDF, spaCy, NLTK
Computer Vision
CNN, ResNet, YOLO, EfficientNet
Predictive ML
Regression, Random Forest, XGBoost
Deep Learning
TensorFlow, Keras, PyTorch
Data Analysis
Pandas, NumPy, Matplotlib, Seaborn
LLM / GenAI
OpenAI API, LangChain, RAG, fine-tuning
Deployment
FastAPI, Django, Docker, AWS Lambda
MLOps
Model versioning, drift detection, retraining
What I deliver
End-to-end model pipelines: data collection, cleaning, feature engineering, model selection, training, and evaluation using scikit-learn, TensorFlow, and Keras.
Text classification, sentiment analysis, fake news detection, named entity recognition, and chatbot development using BERT, LSTM, and transformer models.
Image classification, object detection, and OCR pipelines using Convolutional Neural Networks and pre-trained models (ResNet, EfficientNet, YOLO).
Regression and classification models for business forecasting: sales prediction, churn analysis, pricing optimisation, and demand planning.
Integrating GPT-4, Claude, and open-source LLMs into products: RAG systems, document Q&A, code generation, and custom fine-tuned models.
Wrapping trained models in FastAPI or Django REST endpoints, containerising with Docker, and deploying to production with proper versioning and monitoring.
How I deliver AI
Good machine learning outcomes follow a disciplined process. Here is the end-to-end pipeline I follow for AI development projects in Nepal and internationally.
01
Define the business question, success metrics, and what data exists. Many AI projects fail because the problem is framed as an ML problem when a simpler statistical approach would work better. I give you an honest assessment first.
02
Source, clean, and validate training data. I handle missing values, outlier detection, class imbalance, and data augmentation — the unglamorous 80% of ML work that determines model quality.
03
Transform raw data into features the model can learn from. For text: TF-IDF, word embeddings, BERT encodings. For tabular data: normalization, encoding, and domain-specific feature creation.
04
Select and train appropriate models — from logistic regression baselines to deep neural networks. Evaluate with cross-validation, precision/recall, ROC-AUC, and confusion matrices — not just accuracy.
05
Wrap the trained model in a FastAPI or Django REST endpoint. Containerize with Docker, add input validation, versioning, and response caching. Deploy to AWS or DigitalOcean with monitoring.
06
ML models degrade as data distributions shift. I set up prediction logging, drift detection, and retraining pipelines so your model stays accurate over time without constant manual intervention.
Real applications
Automatically classify user-generated content as appropriate or not — flagging spam, misinformation, or harmful content before it reaches your audience.
Understand how customers feel about your product from reviews, support tickets, and social mentions — at scale, without reading every message manually.
Predict future sales, inventory needs, or resource demand using historical time-series data. Reduces overstock, stockouts, and wasted operational capacity.
Suggest relevant products, content, or connections based on user behaviour — the technology behind e-commerce upselling and content platform engagement.
Classify, detect, or segment objects in images — useful for quality control in manufacturing, medical imaging analysis, or document OCR processing.
Add AI writing assistance, document Q&A, code generation, or customer service chatbots to your product using GPT-4, Claude, or open-source LLMs with RAG architectures.
My AI/ML stack
AI development Nepal
I work on NLP (text classification, sentiment analysis), computer vision (image recognition), predictive analytics, recommendation systems, and LLM integrations. I'm happy to discuss whether AI is the right tool for your specific problem.
Not always. Techniques like transfer learning, data augmentation, and pre-trained models mean you can get good results with relatively small datasets. I'll give you an honest assessment during the discovery phase.
Yes. I deploy ML models as REST APIs and integrate them into existing Django, Next.js, or Flutter applications — so your users never need to know how the magic works under the hood.
Artificial intelligence is the broader field; machine learning is the subset where systems learn from data. In practice, most commercial AI development today involves ML: training models on historical data to make predictions on new data.
Yes. I provide model accuracy metrics, confusion matrices, feature importance reports, and plain-English explanations of what the model does — important for clients who need to justify AI decisions to stakeholders.
Tell me your business problem. I will tell you whether AI is the right solution, and if so, what it would take to build it.
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